ML Engineer - 6 month contract

Opus Recruitment Solutions Ltd; City Of London; Remote; GBP 80,000 - 100,000; Full time

Job summary

A consultancy in the public sector is seeking multiple ML Engineers to work on machine learning models. Candidates must have strong Python skills and experience with frameworks like LangChain or LlamaIndex. This role offers a daily rate of £400 - £500, is remote with occasional travel to London, and provides opportunities to work with advanced AI technologies.

Qualifications

• 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 frameworks like LangChain or LlamaIndex.

Skills

Building and deploying machine learning models

Strong programming skills in Python

Experience with LangChain or LlamaIndex

Working with Large Language Models

Using managed AI services

Conversational AI platforms

Core Python ML libraries (Keras, scikit-learn, Pandas)

Deep learning frameworks (TensorFlow, PyTorch)

Job description

Overview

I am working with a consultancy feeding into the public sector, looking for multiple ML Engineers.

Inside IR35 - £400 - £500 per day (depending on experience level). Remote (occasional London travel).

Required Experience

• 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 (e.g. HuggingFace transformers).
• Practical experience using managed AI services and foundation models from a major cloud provider (e.g., 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).

Desirable (but not essential)

• Working with tools/interfaces for AI applications e.g. 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.