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Senior Full-stack Engineer

Location: Abu Dhabi, UAE
Type: Contract

Role Summary

We are seeking a Senior Full-stack Engineer to build the production-grade, end-to-end application architecture for a large-scale AI transformation.

You will be the architect behind both the "face" and the "nervous system" of our AI agents, designing intuitive, highly responsive user interfaces alongside the secure API gateways and orchestration layers that allow Large Language Models (LLMs) to interact with enterprise systems and sensitive engineering data.

You will act as the technical anchor for key build initiatives across major programmes. Your mandate is to move beyond simple model serving to build robust, on-premise microservices and dynamic frontend applications that power enterprise engineering platforms and autonomous AI agents.

Working within a structured delivery model, you will ensure our software meets strict usability, security, and reliability standards required for highly regulated and mission-critical environments.

Key Responsibilities

End-to-End Agent Architecture & User ExperienceIntuitive AI Interfaces: Architect and develop frontend applications using modern frameworks that allow end-users to interact seamlessly with AI agents. Build custom chat interfaces capable of handling streaming LLM responses, complex citations, and interactive widgets.Agentic Frameworks: Design the backend orchestration layer that coordinates specialised AI workers, managing state, task routing, and error handling.Workflow Logic & State Management: Design task-routing services and frontend state management that map user prompts to specific skills and playbooks, ensuring deterministic and highly responsive execution of critical business and engineering workflows. Enterprise Integration & Data VisualizationSecure Enterprise Connectivity: Build the full-stack Data Access Layer that serves as the secure gateway for AI agents to read and write to enterprise systems, surfacing this data via dynamic dashboards and interactive data visualisations on the client side.RAG Integration & Search UI: Develop the backend logic to interface with vector databases and build frontend search components, enabling AI-powered knowledge and search applications to securely query and render historical and technical documents.External Data Feeds: Construct robust connectors for external data sources to power risk analysis and decision-support applications, building custom UI components to display comparative analysis and trade-offs. Security, Performance & DeploymentEnterprise-Grade Security: Implement full-stack security measures. On the backend, enforce authentication, Role-Based Access Control (RBAC), and sensitive-data redaction. On the frontend, ensure strict protection against XSS, CSRF, and other client-side vulnerabilities.On-Premise Optimization: Engineer systems for strictly isolated or on-premise deployment, optimising client-side bundles and backend resource usage on local computing infrastructure rather than relying on unlimited cloud scaling.Reliability Engineering: Implement comprehensive end-to-end observability, including frontend error tracking, backend logging, tracing, and monitoring in line with enterprise best practices.

Technical Requirements

Frontend Development: Expert proficiency in TypeScript/JavaScript and modern frontend frameworks. Strong experience with complex state management, real-time data streaming, and modern CSS technologies.Backend Development: Expert proficiency in Python for AI integration, alongside experience with modern backend technologies for high-performance microservices.API Architecture: Strong background in designing and consuming RESTful APIs and modern service-to-service communication technologies. Experience building API gateways for traffic management.Database Management: Proficiency with relational databases for transactional data and vector databases for semantic search applications.Containerization & Orchestration: Deep experience with containerisation and orchestration technologies for deploying scalable, full-stack applications in on-premise environments.Integration Protocols: Familiarity with enterprise integration patterns and ERP protocols is a strong plus.

Professional Qualifications

Experience: 5+ years of experience in Full-stack Software Engineering, with a proven track record of building and scaling web applications that serve ML/AI models in production environments.Structured Delivery: Ability to thrive in a structured governance environment, delivering Agile software through sprints and MVPs while meeting rigorous review and systems-engineering requirements.Operational Mindset: Experience building mission-critical systems that require high availability, accessibility, security, and auditability, preferably within highly regulated or complex industries.Cross-Functional Collaboration: Proven track record of bridging the gap between Data Scientists, Backend Engineers, and UI/UX Designers to deliver cohesive end-to-end platforms.

Why This Role?

You are not just building APIs or user interfaces; you are building the "hands" and the "face" of an AI workforce.

Your code will provide the intuitive dashboards and secure backend logic that enable AI agents to support complex business processes, identify critical risks, analyse enterprise information, and validate important engineering decisions.

If you want to build the secure, high-performance, and deeply interactive architecture that makes AI tangible, this role is for you.

GCS is acting as an Employment Business in relation to this vacancy.

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Staff Data Scientist

Location: Abu Dhabi, UAE
Company: Confidential Technology Organization
Project Focus: Advanced AI Systems & Intelligent Supply Chain

Role Summary

We are seeking a Staff Data Scientist to lead the technical execution of high-impact AI initiatives within a large-scale enterprise transformation programme. In this role, you will move beyond experimental modelling to build production-grade systems that support complex engineering, manufacturing, and procurement processes.

You will act as the technical bridge between unstructured data sources (regulatory text, technical documentation) and structured enterprise systems (BIM/IFC models, ERP platforms). Working within a structured delivery framework, you will design and deploy AI solutions supporting two flagship initiatives:

Advanced Engineering AIIntelligent Supply Chain Analytics (predictive spend, forecasting, and risk analytics)

Key Responsibilities

Generative AI & NLP for Engineering AI ProgramsData Exploration & Analysis: Query and analyse large domain-specific datasets from structured and unstructured sources. Identify patterns, features, and data quality requirements prior to model development.Regulatory Text Interpretation: Design and fine-tune Large Language Models (LLMs) to parse complex regulatory and technical documentation and extract structured rules for automated compliance and validation.Rule Formalisation: Convert interpreted regulations into machine-readable formats (such as object-property-condition-value structures) to support automated compliance workflows.Natural Language Querying: Develop methods enabling LLMs to map natural language requirements directly to metadata entities across different schemas.RAG Architecture: Design and implement Retrieval-Augmented Generation (RAG) pipelines capable of querying large repositories of technical documentation and historical project data while minimising hallucinations and maximising accuracy. Predictive Modelling & Supply Chain AnalyticsForecasting Engines: Develop time-series forecasting models to predict spend categories and material demand by combining internal ERP data with external economic indicators.Classification & Risk Scoring: Build machine learning models to classify supplier risks, operational anomalies, and other business-critical events.Data Pipelines: Design robust extraction and transformation pipelines to convert raw data from data platforms, enterprise applications, and external datasets into model-ready features. System Integration & PerformanceModel Orchestration: Collaborate with software engineering teams to integrate AI models into scalable compliance, recommendation, or risk-management platforms accessible through APIs.Performance Optimisation: Ensure AI systems operate efficiently across large datasets through optimisation techniques such as batching, parallelisation, and scalable processing methods.Quality Assurance: Validate model performance against known benchmarks and historical datasets, reducing false positives and false negatives while maintaining enterprise-grade reliability.

Technical Requirements

Core AI / ML: Expert-level Python skills and experience with TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. Strong understanding of supervised and unsupervised learning techniques.NLP & LLMs: Deep experience with transformer-based models (GPT, BERT, Llama, or equivalent), prompt engineering, fine-tuning, and domain adaptation.Data Engineering: Experience working with JSON, XML, SQL, NoSQL databases, and complex data structures. Familiarity with graph-based data models is advantageous.Backend Integration: Understanding of model deployment methodologies and exposure to RESTful API development using Flask, FastAPI, or similar frameworks.Statistics: Strong grounding in statistics, probability, experimentation, model validation, and bias mitigation.

Professional Qualifications

Experience: 5+ years of experience in Data Science, Machine Learning, or Applied AI, with a track record of deploying models into production environments.Domain Adaptability: Ability to rapidly understand complex technical domains and translate business requirements into AI-powered solutions.Structured Delivery: Experience working within Agile, Sprint-based, or structured project delivery environments.Collaboration: Strong communication and stakeholder management skills, with the ability to work effectively alongside technical, operational, and business teams.

Why This Role?

This opportunity provides the chance to build AI solutions that solve complex real-world challenges at enterprise scale. You will play a key role in developing intelligent systems that improve engineering processes, automate decision-making, enhance operational efficiency, and strengthen supply chain resilience. Join a team focused on transforming data into measurable business outcomes.

GCS is acting as an Employment Business in relation to this vacancy.

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QA Automation Engineer, AI & Agentic Systems

Level: Senior IC
Location: Abu Dhabi

Role Summary

We are building agentic AI systems that read complex models, drawings, specifications, and regulatory documents to check compliance, plan and validate schedules, and support design.

This role exists to make those agents trustworthy enough to act on.

This is a senior, hands-on quality role weighted toward testing non-deterministic AI and agentic systems. That is where most of your time sits and where the hiring bar is highest. You will also own the broader quality surface: integration, API, and performance testing are part of the remit, not out of scope.

The differentiator we are hiring for is the ability to define what "good" looks like when a system reasons, calls tools, and can be wrong in subtle ways.

You will scope, build, and run the frameworks yourself, with the independence of a senior engineer, and decide what to build first.

Key Responsibilities

Agentic System Testing (primary focus)Tool-Use & Trajectory Evaluation: Test whether agents select the right tools for the right reasons and follow sound multi-step trajectories, not just whether the final answer looks plausible. Evaluate planning, intermediate steps, and recovery when a tool fails or returns nothing.Grounding & Citation Verification: Verify that agent claims are backed by cited evidence in the source model, drawing, or document, and that a stated location actually supports the answer, catching confident but unsupported outputs.Honest-Failure & Refusal Calibration: Assert that agents ask for clarification or state when something cannot be determined instead of inventing an answer, and build tests where the correct behaviour is refusal.Guardrail & Adversarial Testing: Probe prompt injection, jailbreaks, and instructions hidden inside ingested documents, ensuring the agent treats source content as untrusted data.Multi-Turn & State: Validate follow-ups, references to prior turns, and that conversational state carries correctly across a session. AI & LLM ValidationNon-Deterministic Testing: Architect automated frameworks that score generative-AI outputs for hallucination, consistency, and factual accuracy against gold-standard datasets, using LLM-as-judge methods calibrated against human judgement.Prompt & Model Regression: Design regression suites that catch prompt drift and model version drift, so changes to models or system instructions do not quietly degrade quality. Own the ground-truth and evaluation datasets these depend on. Live Production QualityContinuous Evaluation: Extend evaluation beyond pre-release into production, continuously scoring live agent outputs so quality is measured on real traffic, not only in the test environment.Monitoring & Alerting: Build quality monitoring that flags regressions, drift, and anomalous agent behaviour in production before users or customers do.Quality Incident Response: Triage quality incidents and close the loop, tracing failures in AI logic back to the specific model version or dataset that caused them and feeding fixes into the development cycle. Integration, API & PerformanceBackend, UI & API Testing: Build robust integration tests that validate API integration across services and key user-facing flows.Secure Gateway Validation: Automate testing of secure API gateways, verifying that role-based access controls and sensitive-data redaction logic work correctly before data reaches AI models.Performance & Load: Own performance test plans and their implementation using appropriate performance and load-testing technologies, validating latency, throughput, and stability under realistic load. Data, Traceability & Quality GatesData Validation: Use SQL and data-validation tooling to verify data quality across data platforms and vector databases, including the ground-truth and retrieval corpora the agents depend on.Requirements Traceability: Map test and evaluation cases to system requirements and user needs, producing the verification-and-validation evidence and quality reports needed to ship with confidence.Quality Gates: Enforce quality gates in CI/CD pipelines that prevent non-compliant models or code from merging and prepare readiness evidence for stage and release reviews.

Technical Requirements

AI Evaluation (core): Hands-on experience with LLM and agent-evaluation frameworks or custom Python evaluators, including LLM-as-judge techniques.Agent Observability: Experience tracing and debugging agent runs, including tool calls, intermediate steps, token usage, and latency, using modern agent observability and tracing technologies.Core Automation: Expert Python skills for custom test harnesses and evaluation tooling, plus experience with standard UI and API automation libraries.Performance Testing: Proven ability to craft and implement performance test plans using modern load and performance-testing tools.Data Validation: Proficiency with SQL and data-validation tools, with familiarity with vector databases and retrieval corpora.CI/CD Integration: Experience integrating automated tests and evaluations into CI/CD pipelines and enforcing quality gates.Test Management & Reporting: Experience managing test reports and artifacts and communicating results clearly.Version Control & QE Practices: Experience maintaining code-based frameworks in version control and applying modern quality-engineering practices for fast-paced teams, including shift-left testing, the test pyramid, and automation.Traceability Tools: Familiarity with requirements-management tools and linking results to requirement IDs.

Professional Qualifications

Experience: 5+ years in QA automation or quality engineering, with at least 2 years focused on testing ML models, LLM applications, or AI agents.Probabilistic-Systems Judgement: Able to define pass/fail criteria for systems whose outputs are not identical every run and to communicate confidence levels clearly to engineering leadership.Independent Operator: A senior doer who scopes and builds testing and evaluation frameworks with minimal direction and prioritises what matters first.Collaboration: Works closely with engineering and product, understands existing systems quickly, and moves fast.

Nice to Have

Industry Domain Experience: Familiarity with complex technical data and workflows, including models, drawings, specifications, compliance, and scheduling, is a strong plus.Formal V&V Exposure: Exposure to structured systems-engineering governance, stage-gate reviews, and formal verification and validation is welcome but not required. We value the discipline more than the certification.

GCS is acting as an Employment Business in relation to this vacancy.

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Senior MLOps Engineer

Role Summary

We are seeking a Senior MLOps Engineer to lead the development and management of infrastructure designed for training, deploying, and maintaining ML models. This role plays a critical function in operationalizing state-of-the-art systems to ensure high-performance delivery across research and production environments.

The successful candidate will be responsible for designing and implementing infrastructure to support efficient model deployment, inference, monitoring, and retraining. This includes close collaboration with cross-functional teams to integrate machine learning models into scalable and secure production pipelines, enabling the delivery of real-time, data-driven solutions across various domains.

Key Responsibilities

Inference Serving Across a Wide Model Range: Deploy and scale self-hosted open-weight models from ~7B up to ~376B parameters using engines such as vLLM, Triton, or TGI, choosing serving strategies (continuous batching, tensor/pipeline parallelism, quantization) appropriate to each model's size and SLA.Multi-Provider Gateway & Cost Governance: Operate and improve the routing layer that spans self-hosted models and external APIs (OpenAI, Anthropic, OpenRouter). Build token accounting, budget controls, and cost/latency/quality-aware routing for model consumption predictably and within budget.Training & Fine-Tuning Infrastructure: Build and maintain automated pipelines for fine-tuning, evaluation, versioning, and continuous delivery (MLflow, SageMaker Pipelines, or Kubeflow), including distributed training with DeepSpeed, FSDP, or Accelerate.Reliability & Observability: Own production reliability for ML services, including monitoring, logging, alerting, incident response, and safe rollback, to meet latency, throughput, and availability targets. Participate in on-call for the serving platform.Evaluation & Regression Safety: Stand up evaluation and verification harnesses that catch quality and performance regressions before they reach users, ensuring model or infrastructure changes ship with evidence rather than hope.Platform as a Product: Treat internal engineers and external end-users as customers by reducing friction through sensible defaults, self-service tooling, and clear contracts. Deliver Infrastructure-as-Code (IaC) and CI/CD for repeatable, secure deployments.Model & Cost Efficiency: Apply quantization, pruning, and multi-GPU/distributed inference to reduce latency and cost, especially at the high end of the model range.

Qualifications

Professional Experience: 5+ years in MLOps, ML Infrastructure, or ML Engineering, owning end-to-end model lifecycles in production.Production Ownership: Experience supporting ML services in production and handling real incidents, including degraded inference, GPU OOMs, and cost escalations.Inference Serving Depth: Hands-on experience serving models across a range of sizes, with real decisions made around quantization and parallelism trade-offs under latency and cost constraints.Cost & Capacity Discipline: Demonstrable track record of measuring and optimizing GPU and cloud spend for ML workloads.Programming Skills: Strong Python skills; additional C/C++ experience for performance-sensitive workloads is advantageous.Cloud & Orchestration: Strong experience with cloud services (e.g., AWS SageMaker, EC2, EKS, Lambda), Docker, and Kubernetes. Experience across major hyperscalers is beneficial.Tooling & Distributed Training: Proficiency with MLOps frameworks (MLflow, Kubeflow, or SageMaker Pipelines) and distributed training frameworks (DeepSpeed, FSDP, Accelerate).Bonus: Experience building evaluation and verification harnesses, or multi-provider LLM gateways with token and cost management.Educational Background: Bachelor's or Master's degree in Computer Science, Machine Learning, Data Engineering, or a related field, or equivalent practical experience.

GCS is acting as an Employment Business in relation to this vacancy.

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Senior Machine Learning Engineer
Location: Abu Dhabi, UAE - Remote Global
Type: Contract

Role Summary
We are seeking a Senior Machine Learning Engineer to lead the development and deployment of advanced AI models. In this role, you will be responsible for the end-to-end lifecycle of machine learning systems, from architectural design and data preprocessing to model training, optimization, and production deployment.

You will work at the intersection of generative AI and traditional machine learning, building the engines that power automated requirements engineering via LLMs and predictive risk scoring and demand forecasting solutions. Operating within a structured "Sprint Zero" to "Stage Gate" delivery model, you will ensure models are not just accurate, but also robust, explainable, and deployable within strict security environments.

Key Responsibilities

LLM & NLP PipelinesRegulation Parsing: Design and fine-tune Large Language Model (LLM) pipelines to interpret complex regulatory texts (e.g., military standards, building codes) and extract structured rules.Rule Formalization: Convert natural language requirements into computer-processable formats (e.g., logic tuples) that can be executed by downstream compliance engines.Semantic Search: Implement RAG (Retrieval-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data.Prompt Engineering: Optimize prompt strategies (few-shot learning, chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining. Predictive & Analytical ModelsForecasting Engines: Develop time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals.Risk Scoring: Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.Optimization Algorithms: Design algorithms for multi-objective optimization (e.g., balancing cost vs. lead time vs. risk) to support procurement decision-making. MLOps & ProductionizationModel Deployment: Containerize models using Docker/Kubernetes and deploy them into secure, on-premises inference environments.Pipeline Orchestration: Build automated training and inference pipelines using tools like Kubeflow or MLflow to ensure reproducibility and scalability.Performance Optimization: Optimize model inference latency and resource usage (e.g., quantization, distillation) to run efficiently on available hardware.Monitoring & Retraining: Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement.

Technical Requirements

Core ML/AI: Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy).NLP & GenAI: Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain).MLOps: Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).Data Handling: Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data.Algorithm Design: Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.

Professional Qualifications

Experience: 5+ years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments.Domain Adaptability: Ability to quickly learn and apply ML techniques to specialized domains like defense engineering, supply chain, or construction.Structured Delivery: Experience working in agile environments (Sprints) while adhering to rigorous engineering standards and documentation requirements.Collaboration: Strong communication skills to work effectively with Data Scientists, Backend Engineers, and Domain Experts to align technical solutions with business needs.

Why This Role?

You will be building the intelligence that drives critical national infrastructure. Your models will not just generate text or predictions; they will directly influence the design of complex systems and the resilience of supply chains. If you are ready to apply advanced ML to tangible, high-stakes problems in a rigorous engineering environment, this role is for you.

GCS is acting as an Employment Business in relation to this vacancy.

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Role Summary
We are seeking a Senior Backend Engineer to build the production-grade application architecture for a large-scale AI transformation programme. You will be the architect behind the "nervous system" of AI agents, designing the secure API gateways and orchestration layers that allow Large Language Models (LLMs) to interact with enterprise ERPs and sensitive engineering data.
You will act as the technical anchor for critical build initiatives across flagship programmes. Your mandate is to move beyond simple model serving and build robust, on-premise microservices that power enterprise engineering platforms and autonomous AI agents. Working within a structured "Stage Gate" delivery model, you will ensure software meets strict security and reliability standards required in highly regulated environments.

Key Responsibilities
1. Agent Orchestration & System Architecture
*Agentic Frameworks: Architect the orchestration layer (Agent of Agents) that coordinates specialized AI workers (e.g., Controllership Agent, Treasury Agent), managing state, task routing, and error handling.
*Workflow Logic: Design the "Task Router & Intent Classifier" services that map user prompts to specific skills/playbooks, ensuring deterministic execution of critical financial and engineering workflows.
*Microservices Design: Build scalable, containerized microservices that handle high-concurrency requests, ensuring low-latency responses for real-time market intelligence and design trade-off analysis.
2. Enterprise Integration & API Gateways
*Secure SAP Connectivity: Build the "Data Access Layer" (DAL) that serves as the secure gateway for AI agents to read/write to SAP S/4HANA and Ariba, enforcing strict validation logic before any transaction is committed.
*Scalable Data Lakehouse Connectivity: Build a scalable, secure, observable connection layer to the central data platform, ensuring all read transactions are authenticated, authorized, and properly audited.
*RAG Integration: Develop the backend logic to interface with Vector Databases (e.g., Weaviate) and Retrieval Models, enabling AI-powered knowledge mining solutions to securely query historical proposals and technical documents.
*External API Managers: Construct robust connectors for external data feeds (e.g., S&P Global, Orbis), handling rate limiting, caching, and data normalization.
3. Security, Performance & Deployment
*Enterprise-Grade Security: Implement "Gateway & Policy Guard" services that enforce Authentication, Role-Based Access Control (RBAC), and PII/ITAR redaction before data reaches an LLM.
*On-Premise Optimization: Engineer systems for strictly air-gapped or on-premise deployment, optimizing efficient resource usage on local GPU clusters.
*Reliability Engineering: Implement comprehensive logging, tracing, monitoring, and observability mechanisms while adhering to enterprise best practices and governance standards.
Technical Requirements
*Core Languages: Expert proficiency in Python (FastAPI/Django) for AI integration and Go or Java for high-performance microservices.
*Containerization & Orchestration: Deep experience with Docker and Kubernetes (K8s) for deploying scalable applications in on-premise environments.
*API Architecture: Strong background in designing RESTful APIs and gRPC services. Experience building API Gateways (e.g., Kong, NGINX) for traffic management and security.
*Database Management: Proficiency with Relational Databases (PostgreSQL) for transactional data and Vector Databases (Weaviate, Milvus) for semantic search applications.
*Integration Protocols: Familiarity with enterprise integration patterns and ERP protocols (OData, SOAP) is a strong plus.

Professional Qualifications
*Experience: 5+ years of experience in Backend Engineering, with a focus on building distributed systems or platforms that serve ML/AI models in production.
*Structured Delivery: Ability to thrive in a "Governance Collision" environment, delivering Agile software (Sprints, MVPs) that passes rigorous "Stage Gate" reviews and Systems Engineering audits.
*Operational Mindset: Experience building systems that require high availability and auditability, preferably in Fintech, Healthcare, Defence, or other highly regulated sectors.
*Collaboration: Proven track record of working with Data Scientists to productize models and Frontend Engineers to deliver seamless user experiences.

Why This Role?
You are not just building APIs; you are building the "hands" that allow AI to do real work. Your code will enable AI agents to automate complex workflows, forecast critical operational risks, and support engineering and business decision-making. If you want to build the secure, high-performance architecture that makes AI tangible, this role is for you.

GCS is acting as an Employment Business in relation to this vacancy.

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Senior Data Engineer
Location: Abu Dhabi, UAE - Remote Global
Type: Contract

Role Summary

We are seeking a Senior Data Engineer to architect and build the foundational data infrastructure for
a large-scale AI transformation programme. You will move beyond standard ETL tasks to design and
deploy enterprise-grade Data Lakehouses that serve as the "Single Source of Truth" for AI agents and
advanced analytics platforms.
You will lead the technical execution of data and platform foundation initiatives. Your mandate is to
map rigid enterprise systems (e.g., SAP S/4HANA, Ariba, etc.) and collaborate with teams to
understand complex unstructured data (technical drawings, regulatory text) while building high
performance pipelines and systems that power Intelligent Supply Chain forecasting and generative AI
tools.
You will operate within a structured "Sprint Zero" environment, ensuring data lineage, governance,
and security meet strict enterprise standards.

Key Responsibilities

1. Data Lakehouse Architecture (Supply Chain)
* ERP Integration: Architect and deploy ingestion pipelines to extract high-volume
transactional data from SAP S/4HANA, Ariba, and PLM systems, ensuring near real-time
availability for forecasting models.
* External Feed Integration: Build connectors for external market intelligence feeds (e.g., S&P
Global, Orbis, EcoVadis) to enrich internal procurement data with macroeconomic and
geopolitical signals.
* Unified Data Model: Design and implement a standardized procurement data model and
taxonomy across multiple entities, harmonizing fragmented datasets into a cohesive layer for
analytics.

2. Unstructured Data Pipelines
* Complex Ingestion: Engineer pipelines to ingest and process unstructured technical data,
including PDF tender documents, CAD metadata, and historical CONOPS, transforming them
into vector-ready formats for RAG (Retrieval-Augmented Generation) applications.
* Vector Database Management: Manage and optimize Vector Databases (e.g., Weaviate) to
store embeddings of archival proposals and engineering snippets, ensuring high-speed
retrieval for AI drafting assistants.
* Digital Thread Implementation: Establish data lineage and traceability protocols that link
requirements to physical components, supporting Model-Based Systems Engineering (MBSE)
Digital Thread initiatives.

3. Governance & Security
* Enterprise-Grade Security: Implement Role-Based Access Control (RBAC), audit logging,
and data redaction policies to ensure compliance with export controls and strict on-premise
security requirements.
* Quality Control: Deploy automated data quality frameworks to validate BOM (Bill of
Materials) completeness and cost data accuracy before it reaches AI models.
* Infrastructure Optimization: Optimize pipelines for on-premise GPU clusters and air-gapped
environments, ensuring efficiency within existing infrastructure limits.

Technical Requirements

* Core Stack: Expert proficiency in Python, SQL, and modern data engineering frameworks
(Apache Spark, Kafka, Airflow).
* Enterprise ERP: Strong experience extracting data from complex ERP environments,
specifically SAP S/4HANA and SAP Ariba. Familiarity with SAP BTP is a plus.
* Database Technologies: Deep understanding of Data Lakehouse architectures
(Databricks/Delta Lake), Relational Databases (PostgreSQL), and Vector Databases
(Weaviate/Milvus).
* Data Pipeline Development: Experience building pipelines for RAG solutions, conversational
agents, and classical ML models using tools such as dbt, Dagster, or Prefect.
* DevOps/DataOps: Proficiency with containerization (Docker, Kubernetes) and CI/CD
pipelines for deploying data workflows in secure environments.

Professional Qualifications

* Experience: 5+ years of experience in Data Engineering, with at least 2 years focused on
building pipelines for Machine Learning or Generative AI applications in an enterprise setting.
* Domain Knowledge: Experience in Supply Chain, Manufacturing, or Defence sectors is
highly desirable. Ability to understand Bill of Materials (BOM) structures and procurement
lifecycles.
* Problem Solving: Ability to navigate the "Governance Collision" between agile data work and
rigid systems engineering requirements, ensuring data deliverables meet formal Stage Gate
reviews.
* Collaboration: Proven ability to work alongside Data Scientists and Backend Engineers to
define data schemas that support predictive modelling and AI agents.

Why This Role?

You will be the architect of the data foundation behind advanced AI capabilities. Your work will directly
enable AI agents to support procurement workflows, forecasting, analytics, and engineering decision
making. If you are ready to build the robust infrastructure that turns raw data into strategic advantage,
this role is for you.

GCS is acting as an Employment Business in relation to this vacancy.

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The Role

We are seeking a highly motivated and skilled DevOps Engineer to join our team. You'll play a
crucial role in building, deploying, and maintaining scalable and reliable systems and
infrastructure, working closely with development teams to ensure operational efficiency and
smooth deployment pipelines.

What You'll Do

* Design, implement, and maintain CI/CD pipelines to streamline development
workflows.
* Build and manage scalable infrastructure for AI model deployment and lifecycle
management.
* Automate infrastructure provisioning and management using tools like Terraform,
Ansible, or CloudFormation.
* Optimize cloud-based and on-premises resources for scalability and cost efficiency.
* Manage and fine-tune queuing systems and real-time streaming architectures.
* Monitor and troubleshoot production systems to ensure uptime and performance.
* Implement logging, monitoring, and alerting solutions using tools such as Prometheus,
Grafana, ELK stack, etc.
* Set up comprehensive monitoring for both system metrics and ML model performance.
* Conduct root cause analyses and post-mortems to improve system reliability.
* Collaborate with development and QA teams to deploy new features into production
seamlessly.
* Promote best practices in system architecture, security, and performance.
* Participate in a rotating on-call schedule for production system support.
* Ensure infrastructure complies with security and compliance standards (e.g., SOC2,
ISO27001).
* Securely manage secrets and credentials using tools like Vault or AWS Secrets
Manager.

What You'll Bring
* Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent
experience).
* Proficiency in at least one scripting language: Python, Bash, or Go.
* Hands-on experience with cloud platforms like AWS, Azure, or Google Cloud.
* Skilled in containerization and orchestration with Docker and Kubernetes.
* Experience using CI/CD tools such as Azure DevOps, Jenkins, GitLab CI/CD, or CircleCI.
* Knowledge of monitoring and observability tools like Prometheus, Datadog, New Relic,
Grafana, or PagerDuty.
* Understanding of networking fundamentals including DNS, load balancing, and
firewalls.
* Familiarity with real-time streaming architectures for AI and data applications.

Great Pluses / Preferred Experience
* Experience with Infrastructure as Code (IaC) tools like Terraform or Pulumi.
* Understanding of service mesh technologies like Istio or Linkerd.
* Familiarity with database scaling and administration, including VectorDBs, SQL, and
NoSQL systems.
* Previous experience in a high-traffic production environment

GCS is acting as an Employment Business in relation to this vacancy.

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Senior Full-stack Engineer Location: Abu Dhabi, UAE - Remote Global Type: Contract

Role Summary We are seeking a Senior Full-stack Engineer to build the production-grade, end-to-end application architecture for a large-scale AI transformation. You will be the architect behind both the "face" and the "nervous system" of our AI agents, designing intuitive, highly responsive user interfaces alongside the secure API gateways and orchestration layers that allow Large Language Models (LLMs) to interact with industrial ERPs and sensitive engineering data.

You will act as the technical anchor for key build initiatives across our flagship programs. Your mandate is to move beyond simple model serving to build robust, on-premise microservices and dynamic frontend applications that power enterprise engineering platforms and autonomous AI agents. Working within a structured "Stage Gate" delivery model, you will ensure our software meets the strict usability, security, and reliability standards required for national defense infrastructure.

Key Responsibilities
1. End-to-End Agent Architecture & User Experience
*
Intuitive AI Interfaces: Architect and develop the frontend applications (e.g., React/Next.js) that allow end-users to interact seamlessly with AI agents. Build custom chat interfaces capable of handling streaming LLM responses, complex citations, and interactive widgets.
*
Agentic Frameworks: Design the backend orchestration layer (Agent of Agents) that coordinates specialized AI workers (e.g., Controllership Agent, Treasury Agent), managing state, task routing, and error handling.
*
Workflow Logic & State Management: Design the "Task Router" services and frontend state management that map user prompts to specific skills/playbooks, ensuring a deterministic and highly responsive execution of critical financial and engineering workflows.
2. Enterprise Integration & Data Visualization
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Secure SAP Connectivity: Build the full-stack "Data Access Layer" (DAL) that serves as the secure gateway for AI agents to read/write to SAP S/4HANA and Ariba, surfacing this data via dynamic dashboards and interactive data visualizations on the client side.
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RAG Integration & Search UI: Develop the backend logic to interface with Vector Databases (e.g., Weaviate) and build the frontend search components, enabling the "Knowledge Mining Chatbot" to securely query and render historical proposals and technical documents.
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External Data Feeds: Construct robust connectors for external data (e.g., S&P Global, Orbis) to power Supply Chain Risk Scoring, building custom UI components to display comparative analysis and trade-offs.
3. Security, Performance & Deployment
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Defense-Grade Security: Implement full-stack security measures. On the backend, enforce Authentication, Role-Based Access Control (RBAC), and PII/ITAR redaction. On the frontend, ensure strict protection against XSS, CSRF, and other client-side vulnerabilities.
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On-Premise Optimization: Engineer systems for strictly air-gapped or on-premise deployment, optimizing client-side bundles and backend resource usage on local GPU clusters rather than relying on infinite cloud scaling.
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Reliability Engineering: Implement comprehensive end-to-end observability, including frontend error tracking, backend logging, tracing, and monitoring adhering to enterprise best practices.
Technical Requirements
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Frontend Development: Expert proficiency in TypeScript/JavaScript and modern frontend frameworks (preferably React or Next.js). Strong experience with complex state management, real-time data streaming (WebSockets/SSE), and modern CSS (Tailwind, CSS-in-JS).
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Backend Development: Expert proficiency in Python (FastAPI/Django) for AI integration, and Node.js, Go, or Java for high-performance microservices.
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API Architecture: Strong background in designing and consuming RESTful APIs and gRPC services. Experience building API Gateways (e.g., Kong, NGINX) for traffic management.
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Database Management: Proficiency with Relational Databases (PostgreSQL) for transactional data and Vector Databases (Weaviate, Milvus) for semantic search applications.
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Containerization & Orchestration: Deep experience with Docker and Kubernetes (k8s) for deploying scalable, full-stack applications in on-premise environments.
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Integration Protocols: Familiarity with enterprise integration patterns and ERP protocols (OData, SOAP) is a strong plus.
Professional Qualifications
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Experience: 5+ years of experience in Full-stack Software Engineering, with a proven track record of building and scaling web applications that serve ML/AI models in production environments.
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Structured Delivery: Ability to thrive in a "Governance Collision" environment, delivering Agile software (Sprints, MVPs) that passes rigorous "Stage Gate" reviews and Systems Engineering audits.
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Operational Mindset: Experience building mission-critical systems that require high availability, accessibility, and auditability, preferably in Fintech, Healthcare, or Defense sectors.
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Cross-Functional Collaboration: Proven track record of bridging the gap between Data Scientists, Backend Engineers, and UI/UX Designers to deliver cohesive end-to-end platforms.

Why This Role?

You are not just building APIs or user interfaces; you are building the "hands" and the "face" of an AI workforce. Your code will provide the intuitive dashboards and the secure backend logic that enable AI agents to autonomously draft defense contracts, forecast critical supply shortages, and validate the safety of engineering designs. If you want to build the secure, high-performance, and deeply interactive architecture that makes AI tangible, this role is for you.

GCS is acting as an Employment Business in relation to this vacancy.

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Senior Back-end Engineer
Location: Abu Dhabi, UAE - Remote Global Type: Contract
Role Summary

We are seeking a Senior Back-end Engineer to build the production-grade application architecture for a large-scale AI transformation programme. You will be the architect behind the "nervous system" of AI agents, designing the secure API gateways and orchestration layers that allow Large Language Models (LLMs) to interact with enterprise ERPs and sensitive engineering data.

You will act as the technical anchor for critical build initiatives across flagship programmes. Your mandate is to move beyond simple model serving and build robust, on-premise micro-services that power enterprise engineering platforms and autonomous AI agents. Working within a structured "Stage Gate" delivery model, you will ensure software meets strict security and reliability standards required in highly regulated environments.

Key Responsibilities
1. Agent Orchestration & System Architecture
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Agentic Frameworks: Architect the orchestration layer (Agent of Agents) that coordinates specialised AI workers (e.g., Controllership Agent, Treasury Agent), managing state, task routing, and error handling.
*
Workflow Logic: Design the "Task Router & Intent Classifier" services that map user prompts to specific skills/playbooks, ensuring deterministic execution of critical financial and engineering workflows.
*
Microservices Design: Build scalable, containerized microservices that handle high-concurrency requests, ensuring low-latency responses for real-time market intelligence and design trade-off analysis.
2. Enterprise Integration & API Gateways
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Secure SAP Connectivity: Build the "Data Access Layer" (DAL) that serves as the secure gateway for AI agents to read/write to SAP S/4HANA and Ariba, enforcing strict validation logic before any transaction is committed.
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Scalable Data Lakehouse Connectivity: Build a scalable, secure, observable connection layer to the central data platform, ensuring all read transactions are authenticated, authorized, and properly audited.
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RAG Integration: Develop the backend logic to interface with Vector Databases (e.g., Weaviate) and Retrieval Models, enabling AI-powered knowledge mining solutions to securely query historical proposals and technical documents.
*
External API Managers: Construct robust connectors for external data feeds (e.g., S&P Global, Orbis), handling rate limiting, caching, and data normalization.
3. Security, Performance & Deployment
*
Enterprise-Grade Security: Implement "Gateway & Policy Guard" services that enforce Authentication, Role-Based Access Control (RBAC), and PII/ITAR redaction before data reaches an LLM.
*
On-Premise Optimization: Engineer systems for strictly air-gapped or on-premise deployment, optimizing efficient resource usage on local GPU clusters.
*
Reliability Engineering: Implement comprehensive logging, tracing, monitoring, and observability mechanisms while adhering to enterprise best practices and governance standards.
Technical Requirements
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Core Languages: Expert proficiency in Python (FastAPI/Django) for AI integration and Go or Java for high-performance microservices.
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Containerization & Orchestration: Deep experience with Docker and Kubernetes (K8s) for deploying scalable applications in on-premise environments.
*
API Architecture: Strong background in designing RESTful APIs and gRPC services. Experience building API Gateways (e.g., Kong, NGINX) for traffic management and security.
*
Database Management: Proficiency with Relational Databases (PostgreSQL) for transactional data and Vector Databases (Weaviate, Milvus) for semantic search applications.
*
Integration Protocols: Familiarity with enterprise integration patterns and ERP protocols (OData, SOAP) is a strong plus.
Professional Qualifications
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Experience: 5+ years of experience in Backend Engineering, with a focus on building distributed systems or platforms that serve ML/AI models in production.
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Structured Delivery: Ability to thrive in a "Governance Collision" environment, delivering Agile software (Sprints, MVPs) that passes rigorous "Stage Gate" reviews and Systems Engineering audits.
*
Operational Mindset: Experience building systems that require high availability and auditability, preferably in Fintech, Healthcare, Defence, or other highly regulated sectors.
*
Collaboration: Proven track record of working with Data Scientists to productize models and Front-end Engineers to deliver seamless user experiences.

Why This Role?
You are not just building APIs; you are building the "hands" that allow AI to do real work. Your code will enable AI agents to automate complex workflows, forecast critical operational risks, and support engineering and business decision-making. If you want to build the secure, high-performance architecture that makes AI tangible, this role is for you.

GCS is acting as an Employment Business in relation to this vacancy.

Read less