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

Experis UKlondon, england🇬🇧United Kingdom

Quick overview

SeniorityMid Senior
Employment typeTemporary/Casual
Work modeHybrid
Locationlondon, england, United Kingdom
DockerGCPMicroservicesAWSLLMPyTorchPythonTensorFlowGenerative AIAzureKubernetesMachine LearningCI/CDLangChainLangGraphRAG

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
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