ML Operations Engineer

ML Operations Engineer
Hybrid - Regional Offices
Inside IR35
November start
Contract - 2 Months

• Design, build and deploy AI Copilot agents from development through to production.
• Develop advanced RAG and Graph RAG solutions combining vector search and knowledge graphs.
• Implement agent memory and context management to support persistent, context-aware interactions.
• Build and maintain machine learning training, batch and real-time inference pipelines.
• Integrate enterprise systems and data sources using MCP servers and agent-to-agent (A2A) communication.
• Develop AI evaluation frameworks, regression testing and human review processes.
• Monitor model performance, answer quality and production feedback to drive continuous improvement.
• Maintain model documentation, testing evidence and governance controls.
• Work with business and technical stakeholders to translate requirements into production-ready AI solutions.

• Experience developing and deploying machine learning models in production environments.
• Hands-on experience building AI agents, Copilot solutions and Generative AI applications.
• Strong Python development skills and experience with Databricks.
• Expertise in MLOps, CI/CD, model monitoring and lifecycle management.
• Experience with LLMs, RAG, Graph RAG and vector search.
• Knowledge of batch and real-time inference pipelines.
• Experience with MCP integrations and agent orchestration.
• Familiarity with Kubernetes, Snowflake and enterprise data platforms.
• Understanding of AI security, governance, evaluation and observability.
• Strong stakeholder management and communication skills.
• Financial Services experience is highly desirable.

If you are interested in this opportunity, please apply now with your updated CV in Microsoft Word/PDF format.

Disclaimer
Notwithstanding any guidelines given to level of experience sought, we will consider candidates from outside this range if they can demonstrate the necessary competencies.
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