Machine Learning Engineer - Remote - £55k - £65k base

Job Title: Machine Learning Engineer
Package: £55k - £65k base + 5% pension + expenses
Location: Remote, with occasional client site visits
Interview Process: 1 stage
Experience: 3-4 years
Clearance: BPSS eligible

Role: Machine Learning Engineer (Conversational AI)

Key Responsibilities:

• Design and build sophisticated, agentic AI workflows using frameworks such as LangChain or LlamaIndex to handle complex, multi-step user queries.
• Fine-tune LLM models to improve accuracy, reduce latency, and optimise infrastructure costs.
• Build LLM model evaluation frameworks to ensure stability and reliability across code and model changes.
• Develop and maintain core application logic in Python 3 (using Git) to ensure robust, scalable, and maintainable services.
• Integrate a range of third-party APIs and foundation models from Google Vertex AI, Amazon Bedrock, and Microsoft Azure AI.
• Build secure, performant RESTful APIs using FastAPI or Django REST Framework to connect AI services with Back End government systems.
• Work with vector databases and retrieval mechanisms to provide accurate, up-to-date context for AI agents.
• Collaborate in a multi-disciplinary team to continuously improve the agent's performance, reasoning, and reliability.

We're looking for passionate individuals with Generative AI experience and a desire to make an impact in the public sector.

You'll bring:

• Proven experience building and deploying machine learning models in production.
• Strong programming skills and deep expertise in Python.
• Hands-on experience with agentic or RAG frameworks (LangChain, LlamaIndex).
• Familiarity with LLM APIs or local frameworks (eg, HuggingFace Transformers).
• Practical experience using managed AI services and foundation models from major cloud providers.
• Experience with conversational AI platforms (Dialogflow, Lex, Rasa, etc.).
• Solid knowledge of ML libraries (Keras, scikit-learn, Pandas) and deep learning frameworks (TensorFlow, PyTorch).
• The ability to explain complex concepts clearly to both technical and non-technical audiences.
• A collaborative, humble approach with an eagerness to mentor others.
• Comfort working in ambiguous, fast-paced environments.

Desirable (Not Essential):

• Experience with AI application interfaces (MCP protocol).
• Training ML/DL models using Axolotl, LoRA, or QLoRA.
• Multi-agent orchestration experience (LangGraph, AutoGen, CrewAI).
• Familiarity with observability tools like TruLens or Helicone.
• Knowledge of AI safety frameworks (eg, Guardrails AI).