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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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QA Automation Engineer - AI & Agentic Systems

Level: Senior IC
Location: Remote

Role Summary

We are building agentic AI systems that can interpret complex data sources, documentation, and structured information to perform analysis, validation, and decision-support tasks. 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 for this role 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 source data, documents, or knowledge bases, and that referenced locations actually support the answer, catching confident but unsupported outputs.Honest-Failure & Refusal Calibration: Assert that agents ask for clarification or respond appropriately when information cannot be confidently determined, rather than inventing answers.Guardrail & Adversarial Testing: Probe prompt injection, jailbreaks, and instructions hidden within ingested content, 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.

2. 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, ensuring 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 usage, not only in test environments.Monitoring & Alerting: Build quality monitoring that flags regressions, drift, and anomalous agent behaviour before users discover them.Quality Incident Response: Triage quality incidents and trace failures back to specific model versions, prompts, or datasets, 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 Control (RBAC) and PII-redaction logic work correctly before data reaches AI models.Performance & Load: Own performance test plans and implementation (for example, Locust, JMeter, k6), validating latency, throughput, and stability under realistic load. Data, Traceability & Quality GatesData Validation: Use SQL and data-validation tooling (for example, Great Expectations) to verify data quality across data platforms and vector databases, including the ground-truth and retrieval corpora that agents depend on.Requirements Traceability: Map test and evaluation cases to system requirements and user needs, producing verification-and-validation evidence and quality reports needed to ship with confidence.Quality Gates: Enforce quality gates in GitLab CI/CD that prevent non-compliant models or code from merging and prepare readiness evidence for release reviews.

Technical Requirements

AI Evaluation (Core): Hands-on experience with LLM/agent evaluation frameworks (e.g., DeepEval, TruLens, RAGAS, or custom Python evaluators) and LLM-as-judge techniques.Agent Observability: Experience tracing and debugging agent runs, including tool calls, intermediate steps, token usage, and latency, using tools such as LangSmith, Langfuse, or OpenTelemetry-based tracing.Core Automation: Expert Python for custom test harnesses and evaluation tooling (Pytest), plus standard automation libraries (Selenium/Playwright for UI, Requests for API).Performance Testing: Proven ability to design and implement performance test plans (e.g., Locust, JMeter, k6).Data Validation: Proficiency with SQL and data-validation tools, and familiarity with vector databases and retrieval corpora.CI/CD Integration: Integrating automated tests and evaluations into GitLab CI/CD pipelines and enforcing quality gates.Test Management & Reporting: Managing test reports and artifacts (e.g., TestRail, Allure) and communicating results clearly.Version Control & QE Practices: Maintaining code-based frameworks in Git/GitLab and applying modern quality-engineering practices.Traceability Tools: Familiarity with requirements-management tools (e.g., Jira, Linear, Jama, Polarion) 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 communicate confidence levels clearly to engineering leadership.Independent Operator: A senior individual contributor who scopes and builds testing and evaluation frameworks with minimal direction and prioritises what matters most.

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

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Job Description

- Implements, refines, and validates machine learning algorithms for products and applications. - Implements data pipelines consisting of data ingest, data validation, data cleaning, and data monitoring.

- Trains machine learning models, validates the accuracy of the machine learning models once trained, and deploys validated machine learning models into production.

- Assists in development of proof of concept solutions and contributes to studies to support future product or application development.

- Researches, writes, and edits documentation and technical requirements, including evaluation plans, confluence pages, white papers, presentations, test results, technical manuals, formal recommendations, and reports.

- Tests and evaluates solutions. Completes case studies, testing, and reporting.

Skills

- Bachelor's degree in computer science, computer engineering, mathematics, related technical discipline, or related industry experience

- Experience with machine learning, deep learning, data mining, and/or statistical analysis tools and how to deploy and monitor machine learning models.

- Strong programming and software development skills and familiarity with Python, Java or Scala.

- Knowledge of data pipeline and cloud technologies such as Kafka, Spark, and Docker.

- 1-3 years related experience after Bachelors.

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

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