AI / Machine Learning Engineer – Agentic LLM Systems (Contract)

Experis UK; Greater London; Hybrid; GBP 80,000 - 120,000; Part time

Benefits offered by this job

6 Months Initial Contract

Hybrid | London

Job summary

Experis UK seeks an AI / Machine Learning Engineer to design and scale agentic LLM systems for production environments. You will build tools, workflows, and infrastructure, integrating with major LLM providers and enterprise data pipelines.

Responsibilities include developing RAG pipelines, embeddings workflows, and backend services, plus diagnosing failures in agent-generated code. This contract role offers a hybrid London setup with extensions based on performance.

Qualifications

• 4+ years’ experience in ML/AI engineering (LLMs, recommender systems, optimisation, or similar).
• Proven hands-on experience building LLM agents, RAG systems, or orchestrated AI workflows in production.
• Strong Python (with PyTorch, TensorFlow, or similar frameworks).
• Experience with agent frameworks (LangChain, LangGraph, AutoGen, or similar).
• Solid understanding of APIs, backend services, and microservices architectures.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerised systems (Docker, Kubernetes).
• Familiarity with event-driven or serverless architectures.
• Experience with CI/CD pipelines, testing, and infrastructure as code.
• Experience running and analysing large-scale experiments and performance metrics.
• Exposure to monitoring/observability tools for production systems.

Responsibilities

• Design and build tools, workflows, and infrastructure for agentic LLM systems.
• Develop RAG pipelines, embeddings workflows, and retrieval systems for enterprise data.
• Diagnose and fix failures in agent-generated code and workflows.
• Build frameworks for tool-calling, multi-step planning, orchestration, and agent routing.
• Integrate with LLM providers (OpenAI, Anthropic, Vertex AI, open-source) and support multi-model usage.
• Develop backend services and APIs to support AI-driven applications and workflows.
• Run experiments to improve reliability, latency, cost, and success rates.
• Contribute to evaluation frameworks, observability, and monitoring of LLM/agent performance.

Skills

Python

LLM agents

RAG systems

APIs & microservices

Cloud platforms

CI/CD & IaC

Observability

Experimentation & metrics

Tools

LangChain

LangGraph

AutoGen

Docker

Kubernetes

Pinecone

Weaviate

Job description

AI / Machine Learning Engineer – Agentic LLM Systems (Contract)

We’re working with a leading tech organisation building next-generation agentic AI systems – LLMs that can plan, reason, call tools, and write/execute code. We’re hiring a Machine Learning Engineer to help design, build, and scale these systems into production-grade, enterprise environments.

You will:

• Design and build tools, workflows, and infrastructure for agentic LLM systems
• Develop RAG pipelines, embeddings workflows, and retrieval systems for enterprise data
• Work with researchers and engineers to diagnose and fix failures in agent-generated code and workflows
• Build frameworks for tool-calling, multi-step planning, orchestration, and agent routing
• Integrate with LLM providers (OpenAI, Anthropic, Vertex AI, open-source models) and support multi-model usage
• Develop backend services and APIs to support AI-driven applications and workflows
• Build and run experiments to improve reliability, latency, cost, and success rates
• Contribute to evaluation frameworks, observability, and monitoring of LLM/agent performance

What We’re Looking For:

• 4+ years’ experience in ML/AI engineering (LLMs, recommender systems, optimisation, or similar)
• Proven hands‑on experience building LLM agents, RAG systems, or orchestrated AI workflows in production
• Strong Python (with PyTorch, TensorFlow, or similar frameworks)
• Experience with agent frameworks (LangChain, LangGraph, AutoGen, or similar)
• Solid understanding of APIs, backend services, and microservices architectures
• Experience working with cloud platforms (AWS, GCP, or Azure) and containerised systems (Docker, Kubernetes)
• Familiarity with event-driven or serverless architectures
• Experience with CI/CD pipelines, testing, and infrastructure as code
• Experience running and analysing large-scale experiments and performance metrics (latency, accuracy, cost)
• Exposure to monitoring/observability tools for production systems

Nice to Have:

• Experience with multi-agent systems or distributed AI architectures
• Familiarity with vector databases (Pinecone, Weaviate, OpenSearch, etc.)
• Experience integrating AI systems into enterprise platforms or business workflows
• 6 Months Initial Contract | + Extensions
• Hybrid | London