Machine Learning Engineer - Hybrid (London) - £525/day - BPSS Required

Layer7 • APPLICATION • London, Not Specified, United Kingdom • 6h ago

Location: Hybrid | London
Rate: £525 per day Inside IR35
Clearance: BPSS
Duration: Contract

Overview

We're seeking an experienced Machine Learning Engineer to join a dynamic team delivering cutting-edge AI and machine learning solutions. This is an exciting opportunity to contribute to the design, development, and deployment of intelligent systems leveraging Large Language Models (LLMs), RAG frameworks, and agentic AI architectures.

Key Responsibilities

Design, build, and deploy machine learning models into production environments.
Work with agentic AI and Retrieval-Augmented Generation (RAG) frameworks such as LangChain or LlamaIndex.
Integrate and fine-tune Large Language Models (LLMs) using APIs or local frameworks (eg, HuggingFace Transformers).
Utilise managed AI services from cloud providers (eg, Amazon Bedrock, Google Vertex AI, Azure AI Services).
Contribute to conversational AI systems using tools such as Google Dialogflow, Amazon Lex, or Rasa.
Apply core Python ML libraries and frameworks - Keras, scikit-learn, Pandas, TensorFlow, and PyTorch.
Collaborate with design and user research teams to ensure technically sound and user-centric solutions.
Communicate complex concepts clearly to both technical and non-technical stakeholders.
Mentor team members and foster a culture of learning, collaboration, and empathy.

Essential Skills & Experience

Proven experience building and deploying ML models in production.
Deep expertise in Python programming.
Hands-on experience with LangChain, LlamaIndex, or similar frameworks.
Familiarity with LLM APIs, transformer models, and cloud AI platforms.
Strong grasp of machine learning fundamentals and modern deep learning techniques.
Excellent communication skills and ability to work in an agile, dynamic environment.

Desirable Experience

Exposure to AI application protocols like MCP.
Experience training models using Axolotl, LoRA, or QLoRA.
Knowledge of multi-agent orchestration tools (eg, LangGraph, AutoGen, CrewAI).
Familiarity with LLM observability tools (eg, TruLens, Helicone).
Understanding of AI safety and reliability frameworks, such as Guardrails AI.

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