MLOps Engineer

Role: MLOps Engineer - Contract Location: London | Hybrid - 3/4 days in client office. Industry: Financial Services / Banking Start Date: Nov 2026 Agreed Contract Rate (ACR): £308.33 per day Payroll provider: Rockford Payroll Info for Contingent Workers – Rockford Pay Daily Rate: £400 Inside IR35 Daily Rate breakdown Traditional Daily Rate: Up to £400 per day, dependent on experience Equivalent Annual Salary: £71,531.56 + £8,633.12 holiday pay The Role We are looking for experienced MLOps Engineers to join a leading global financial services organisation, working on cutting-edge Generative AI, machine learning and intelligent automation solutions. This is an exciting opportunity to design, develop and deploy production-ready AI agents, advanced retrieval systems and scalable machine learning pipelines within a complex enterprise environment. You will play a key role in delivering secure, reliable and high-performing AI solutions, working across MLOps, cloud infrastructure, data engineering and large language model technologies.

Key Responsibilities

• Design, develop and deploy production-ready AI agents and copilot solutions, taking ownership from initial prototype through to production.
• Build advanced retrieval-augmented generation (RAG) solutions, combining knowledge graphs, vector search and retrieval optimisation techniques.
• Develop agent memory and context management capabilities to support accurate, secure and personalised AI interactions.
• Design, build and maintain scalable machine learning training and inference pipelines using Databricks.
• Develop and productionise predictive ML models for propensity scoring, next-best-action recommendations, churn prediction and cross-selling opportunities.
• Implement batch and real-time inference solutions, optimising performance, latency and model explainability.
• Build and maintain AI evaluation frameworks, including automated testing, regression testing, hallucination detection and response quality monitoring.
• Develop multi-agent workflows and integrate enterprise systems using Model Context Protocol (MCP) and agent-to-agent (A2A) communication.
• Monitor production AI solutions, incorporating user feedback to improve model performance, retrieval accuracy and reliability.
• Deploy and manage AI/ML applications across AWS and/or Azure cloud environments.
• Ensure solutions meet enterprise security, governance, model risk and regulatory requirements.
• Collaborate with engineering, product and business stakeholders to deliver scalable AI solutions.

Required Experience & Skills Required

• Strong hands-on experience in MLOps, machine learning engineering or production AI development .
• Proficiency in Python and experience developing, deploying and maintaining machine learning models in production.
• Experience building and managing end-to-end ML pipelines, including feature engineering, model training, deployment and monitoring.
• Practical experience with Generative AI, Large Language Models (LLMs), RAG and AI agent architectures .
• Experience working with Databricks and large-scale data processing frameworks.
• Strong understanding of vector databases, embeddings, retrieval techniques and model evaluation.
• Experience developing APIs and integrating AI/ML solutions with enterprise systems.
• Hands-on experience with AWS and/or Azure cloud environments.
• Knowledge of CI/CD, automated testing, containerisation and production deployment practices.
• Strong understanding of model performance monitoring, observability, security and operational resilience.
• Experience working within Agile development environments and collaborating with cross-functional teams.

Nice to Have

• Experience with Graph RAG, knowledge graphs and advanced retrieval architectures .
• Knowledge of agent memory frameworks, including episodic and semantic memory.
• Hands-on experience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) frameworks.
• Experience with LLM evaluation techniques, LLM-as-a-judge, groundedness testing and hallucination detection.
• Familiarity with Snowflake, Kubernetes, Splunk, Confluence and Bitbucket.
• Experience building recommendation engines, propensity models or next-best-action solutions.
• Knowledge of online experimentation, A/B testing and feedback-driven model optimisation.
• Experience with model governance, explainability, risk controls and responsible AI.
• Previous experience within financial services, investment banking, payments or another highly regulated environment.

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