Contract - Machine Learning Operations Engineer

Deloitte (UK); Birmingham; Hybrid; GBP 92,000 - 148,000; Full time

Job summary

Deloitte is seeking a Machine Learning Operations Engineer (contract) to design, build and deploy enterprise AI and Copilot solutions across financial services clients. You will work across ML pipelines, RAG, agent orchestration and cloud platforms to deliver production-grade capabilities with strong SDLC, security and observability.

Hybrid work across Deloitte offices, travel to client sites three days a week, duration two months with ASAP November 2026 start.

Qualifications

• 4+ years building Copilot or AI agents in a Financial Services background.
• 4+ years deploying ML models in production.
• Experience with AWS cloud platforms and ML Ops practices.
• Knowledge of vector search, RAG and AI governance.
• Strong Python development and data engineering integration skills.

Responsibilities

• Design, build and own production-grade Copilot agents for enterprise use.
• Deliver ML and retrieval capabilities turning data into explainable recommendations.
• Establish governance, evaluation and controls for trusted AI at scale.

Skills

Python

ML Ops

RAG

Agent orchestration

Cloud platforms

Security

Observability

Stakeholder mgmt

Tools

AWS

Databricks

Kubernetes

Snowflake

Job description

Birmingham, Bristol, Cardiff, Edinburgh, Glasgow, Leeds, London, Manchester

Business Line

Job Type

Contract

Date published

22-Oct-2026

25582

Job Title: Machine Learning Operations Engineer (contract)

Location: Hybrid - open to any Deloitte office location (London, Bristol, Birmingham, Manchester, Leeds, Cardiff, Edinburgh, Glasgow). Travel to office 3 days per week

Duration: 2 months

Contract Start Date: ASAP November 2026

Day Rate: Competitive (Inside IR35)

Working with the Deloitte Associate (Contractor) Programme means we can offer you the opportunity to work on a variation of industry and client related projects. Our aim is to retain the best talent and so when your project end date nears our team of Talent Community Advisors will be working with you to look at alternative projects within the firm that suit your experience should you wish to continue with Deloitte.

The Role

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;

• Design and ship production Copilot agents, owning them from prototype through production
• Build retrieval that holds up in production: Graph RAG combining knowledge-graph traversal over accounts, contacts, products, and prior engagements with vector search,
• Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case,
• Own organizational context management
• Build and productionize ML model training pipelines
• Build batch and online inference for those modelsOwn answer quality as an engineering discipline
• 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).
• 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.
• Close the loop with production feedback
• Produce the model documentation, test evidence and controls
• Deploy and operate solutions on public cloud (AWS) with strong SDLC, security, resiliency, and observability practices.
• Partner with sales, coverage, product and business partners to turn use cases into shipped, supported agents.

Essential Skills and Experience

• > 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 AWScloud 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

Deliverables: Responsibilities but not limited to:

• Design, build and own production-grade copilot agents that provide secure, context-aware and persistent support across the client relationship.
• Deliver production ML and retrieval capabilities that turn enterprise data into timely, explainable recommendations for sellers.
• Establish the evaluation, governance and operational controls required for trusted AI at enterprise scale.

IR35

As a means of managing tax, commercial and reputational risks, Deloitte prohibits the use of Associates through Personal Service Companies (‘PSCs’). All Associates must contract under PAYE arrangements through a Deloitte approved ‘Employment Company’ (aka ‘umbrella company.’)