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# Machine Learning Operations Engineer

- **Company:** Deloitte
- **Location:** Birmingham, UK
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** AI Evaluation, Artificial Intelligence, Amazon Web Services, Confluence, Continuous Integration, Monitoring of Systems, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Reference Data, Microsoft Copilot, Search Technologies, Enterprise Data Management, Large Language Models, Snowflake, Multi-Agent Systems, Generative AI, Kubernetes, Real-time Inference, Bitbucket, Machine Learning Operations, Virtual Agents, Graph RAG, Splunk, Network Server, Databricks, Agent2Agent Protocol
- **Published:** October 23, 2026
- **Apply:** https://apply.deloitte.co.uk/UKCareers/Login?jobId=25582

## About the Role

* > 4 years experience building Copilot or AI agent solutions preferably from a Financial Services background
 * > 4 years experience building and deploying machine learning models in production environments.
 * Experience working with AWS cloud platforms.
 * Expertise in ML Ops practices, CI/CD, model monitoring and lifecycle management.
 * Experience with Generative AI, LLMs and Retrieval-Augmented Generation (RAG) architectures.
 * Strong Python development skills and experience with Databricks.
 * Experience developing batch and real-time inference pipelines.
 * Knowledge of vector search, retrieval optimisation and AI evaluation frameworks.
 * Experience of Graph RAG and MCP integrations.
 * Familiarity with Kubernetes, Snowflake and enterprise data integrations.
 * Strong understanding of security, observability and AI governance principles.
 * Excellent stakeholder management and communication skills

## Description

We are looking for an experienced Machine Learning Operations Engineer to help design, build and deploy enterprise AI and Copilot solutions for one of our Financial Services clients. You will work across machine learning, retrieval-augmented generation (RAG), agent orchestration and cloud platforms to deliver scalable, production-grade AI capabilities. The role requires hands-on experience building ML pipelines, deploying models into production, integrating enterprise data sources and implementing robust governance, monitoring and evaluation frameworks. Preferably someone from a Financial Services background.

Responsibilities but not limited to;

 1. Design and ship production Copilot agents, owning them from prototype through production
 2. Build retrieval that holds up in production: Graph RAG combining knowledge-graph traversal over accounts, contacts, products, and prior engagements with vector search,
 3. Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case,
 4. Own organizational context management
 5. Build and productionize ML model training pipelines
 6. Build batch and online inference for those models
 7. Own answer quality as an engineering discipline
 8. Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers (CRM, client and product reference data, document stores, calendar and email, Databricks, Snowflake, Kubernetes, Confluence, Bitbucket, Splunk).
 9. Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, human review loops with sellers, and quality and safety gating before release.
 10. Close the loop with production feedback
 11. Produce the model documentation, test evidence and controls
 12. Deploy and operate solutions on public cloud (AWS) with strong SDLC, security, resiliency, and observability practices.
 13. Partner with sales, coverage, product and business partners to turn use cases into shipped, supported agents., 1. Design, build and own production-grade copilot agents that provide secure, context-aware and persistent support across the client relationship.
 2. Deliver production ML and retrieval capabilities that turn enterprise data into timely, explainable recommendations for sellers.
 3. Establish the evaluation, governance and operational controls required for trusted AI at enterprise scale.