Machine Learning Engineer - 6 months - Remote

Role: Machine Learning Engineer
Rate: £600 Inside IR35
Location: Remote (With Occasional visits)
Start Date: ASAP
Clearence: BPSS

As a Machine Learning Engineer (Conversational AI) you will:

• Design and build sophisticated, agentic AI workflows using frameworks like LangChain or LlamaIndex to handle complex, multi-step user queries.
• Fine-tune LLM models to improve accuracy, reduce latency, and optimize infrastructure costs.
• Build LLM model evals for more stable, reliable application that is resilient to code and model changes.
• Develop, review, and maintain the core application logic in Python 3 and Git, ensuring the service is robust, scalable, and maintainable.
• Integrate a wide range of services, including third-party APIs and foundation models from hyperscalers like Google (Vertex AI), Amazon (Bedrock), and Microsoft (Azure AI).
• Build secure and performant RESTful APIs using Python frameworks like FastAPI or Django REST Framework to connect the AI service with Back End government systems.
• Work with vector databases and retrieval mechanisms to provide the AI agent with accurate, up-to-date information.
• Collaborate in a multi-disciplinary team to continuously improve the agent's performance, reasoning capabilities, and reliability.

Who you are:

We're looking for people with a passion for public service and Generative AI skills to make a difference. You will have:

• Proven experience building and deploying machine learning models in a production environment.
• Strong programming skills and deep expertise in Python.
• Hands-on experience building with agentic or RAG (Retrieval-Augmented Generation) frameworks like LangChain or LlamaIndex.
• Familiarity with tools for working with Large Language Models via API or in a local context (eg HuggingFace transformers).
• Practical experience using managed AI services and foundation models from a major cloud provider (eg, Amazon Bedrock, Google Vertex AI, Azure AI Services).
• Experience with a major conversational AI platform (Google Dialogflow, Amazon Lex, Rasa, or similar).
• A solid understanding of core Python ML libraries (Keras, scikit-learn, Pandas) and deep learning frameworks (TensorFlow, PyTorch).
• Ability to explain complex technical concepts to both technical and non-technical audiences.
• A humble attitude and eagerness to help and mentor others with empathy.
• Ability to navigate ambiguity and prioritise effectively in dynamic environments.
• Collaborating with design and user research disciplines to deliver valuable product outcomes.

Desirable (but not essential) experience:

• Working with tools/interfaces for AI applications eg MCP protocol.
• Training traditional ML and DL models using tools like Axolotl, LoRA, or QLoRA.
• Experience with multi-agent orchestration frameworks (LangGraph, AutoGen, CrewAI)
• Experience with observability and evaluation tools for LLMs such as TruLens or Helicone.
• Experience with AI safety and reliability frameworks like Guardrails AI.