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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 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
*
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
*
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 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

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.

Read less

Senior Backend Cloud Engineer

Location: Cork, Ireland (on-site)
Type: Full-time, Permanent

About the company
A fast-growing, IDA-backed industrial technology company building out its engineering presence in Cork. The business operates at the forefront of emerging technology, delivering cloud and edge platforms that power smart, connected building and facilities management solutions for enterprise clients globally.

About the role
We're looking for a Senior Backend Cloud Engineer to help design, build, and scale the backend services behind our cloud and edge platform. You'll own services end-to-end - from architecture through deployment - in a genuinely cloud-native environment built on Microsoft Azure and Kubernetes.

Responsibilities

Design, develop, and maintain cloud-native backend services using C# and .NET (Core/6+)Build scalable microservices architectures deployed on Azure Kubernetes Service (AKS)Containerise services with Docker and manage deployments using KubernetesImplement event-driven systems leveraging Azure services (Event Hub, Service Bus)Develop and optimise RESTful APIs with proper versioning, pagination, and error handlingModel data and implement persistence using Entity Framework and relational databasesAutomate deployments using ArgoCD, Bicep (IaC), and GitOps workflowsCollaborate with front-end developers and cloud architects to deliver end-to-end solutions

Requirements

5+ years' professional backend/cloud development experienceStrong C#, .NET (Core/6+) and API design skillsHands-on experience with Azure services (AKS, App Services, Functions, Event Hub, Service Bus, Key Vault)Solid understanding of microservices and containerisation (Docker, Kubernetes)Experience with Entity Framework and relational databases (SQL Server, PostgreSQL)Familiarity with CI/CD and deployment automation (ArgoCD, Azure DevOps/GitHub Actions)Knowledge of Infrastructure-as-Code (Bicep or ARM) and GitOps workflows

Nice to have

IoT data ingestion and edge-to-cloud integration experienceWorking knowledge of NoSQL databases (Cosmos DB, Redis)Cloud security best practices (OAuth2/OIDC, secrets management)

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

Read less