MLOps Engineer

Deloitte - Recruitment; Greater London; Hybrid; GBP 70,000 - 77,000; Full time

Job summary

Deloitte is seeking an experienced MLOps Engineer for a contract role in London, hybrid 3/4 days in client office. You will design, develop and deploy production-ready AI agents and scalable ML pipelines in a regulated financial services environment.

The role covers Generative AI, retrieval systems, and enterprise integrations across AWS/Azure, with emphasis on security, governance, and observable ML systems.

Qualifications

• Strong hands-on experience in MLOps or production AI development.
• Proficiency in Python and deploying production ML models.
• Experience building end-to-end ML pipelines incl. training, deployment, monitoring.
• Experience with Generative AI, LLMs, RAG and AI agent architectures.
• Experience with Databricks and large-scale data processing.
• Knowledge of vector databases, embeddings and evaluation.
• Experience with AWS and/or Azure cloud environments.
• CI/CD, automated testing, containerisation and production deployment.
• Model performance monitoring, observability and security.

Responsibilities

• Design, develop and deploy production-ready AI agents and copilot solutions.
• Build RAG solutions using knowledge graphs and vector search.
• Develop agent memory and context management for personalised AI interactions.
• Design scalable ML pipelines using Databricks for training and inference.
• Develop production-grade ML models for propensity scoring and churn prediction.
• Implement batch and real-time inference with performance optimization.
• Maintain AI evaluation frameworks with automated testing and monitoring.
• Collaborate with engineering and business stakeholders to deliver scalable AI.
• Deploy and manage AI/ML apps on AWS and/or Azure.
• Ensure compliance with security and regulatory requirements.

Skills

MLOps

Python

ML pipelines

Databricks

AWS

Azure

CI/CD

Containerization

LLMs

RAG

A2A

MCP

Security

Agile

Tools

Snowflake

Kubernetes

Splunk

Confluence

Bitbucket

Job description

London, United Kingdom | Posted on 16/10/2026

Role: MLOps Engineer- ContractLocation: London |Hybrid- 3/4 days in client office.Industry: Financial Services / BankingStart Date: Nov 2026Agreed Contract Rate (ACR): £308.33 per dayPayroll provider:RockfordPayroll Info for Contingent Workers – Rockford Pay Daily Rate: £400 Inside IR35Daily Rate breakdownTraditional Daily Rate: Up to £400 per day, dependenton experienceEquivalent Annual Salary: £71,531.56 + £8,633.12holiday pay

The Role

Weare looking for experienced MLOps Engineers to join a leadingglobal financial services organisation, working on cutting-edge Generative AI,machine learning and intelligent automation solutions. This is an excitingopportunity to design, develop and deploy production-ready AI agents, advancedretrieval systems and scalable machine learning pipelines within a complexenterprise environment. You will play a key role in delivering secure, reliableand high-performing AI solutions, working across MLOps, cloud infrastructure,data engineering and large language model technologies.

Key Responsibilities

• Design, develop and deployproduction-ready AI agents and copilot solutions, taking ownership frominitial prototype through to production.
  

• Build advancedretrieval-augmented generation (RAG) solutions, combining knowledgegraphs, vector search and retrieval optimisation techniques.
  

• Develop agent memory andcontext management capabilities to support accurate, secure andpersonalised AI interactions.
  

• Design, build and maintainscalable machine learning training and inference pipelines usingDatabricks.
  

• Develop and productionisepredictive ML models for propensity scoring, next-best-actionrecommendations, churn prediction and cross-selling opportunities.
  

• Implement batch and real-timeinference solutions, optimising performance, latency and modelexplainability.
  

• Build and maintain AIevaluation frameworks, including automated testing, regression testing,hallucination detection and response quality monitoring.
  

• Develop multi-agent workflowsand integrate enterprise systems using Model Context Protocol (MCP) andagent-to-agent (A2A) communication.
  

• Monitor production AIsolutions, incorporating user feedback to improve model performance,retrieval accuracy and reliability.
  

• Deploy and manage AI/MLapplications across AWS and/or Azure cloud environments.
  

• Ensure solutions meetenterprise security, governance, model risk and regulatory requirements.
  

• Collaborate with engineering,product and business stakeholders to deliver scalable AI solutions.
  

Experience & Skills Required

• Strong hands-on experience in MLOps, machine learningengineering or production AI development .
  

• Proficiency in Python andexperience developing, deploying and maintaining machine learning modelsin production.
  

• Experience building andmanaging end-to-end ML pipelines, including feature engineering, modeltraining, deployment and monitoring.
  

• Practical experience with Generative AI, Large LanguageModels (LLMs), RAG and AI agent architectures .
  

• Experience working withDatabricks and large-scale data processing frameworks.
  

• Strong understanding of vectordatabases, embeddings, retrieval techniques and model evaluation.
  

• Experience developing APIs andintegrating AI/ML solutions with enterprise systems.
  

• Hands-on experience with AWSand/or Azure cloud environments.
  

• Knowledge of CI/CD, automatedtesting, containerisation and production deployment practices.
  

• Strong understanding of modelperformance monitoring, observability, security and operational resilience.
  

• Experience working within Agiledevelopment environments and collaborating with cross-functional teams.
  

Nice to Have

• Experience with Graph RAG, knowledge graphs andadvanced retrieval architectures .
  

• Knowledge of agent memoryframeworks, including episodic and semantic memory.
  

• Hands-on experience with ModelContext Protocol (MCP) and Agent-to-Agent (A2A) frameworks.
  

• Experience with LLM evaluationtechniques, LLM-as-a-judge, groundedness testing and hallucinationdetection.
  

• Familiarity with Snowflake,Kubernetes, Splunk, Confluence and Bitbucket.
  

• Experience buildingrecommendation engines, propensity models or next-best-action solutions.
  

• Knowledge of onlineexperimentation, A/B testing and feedback-driven model optimisation.
  

• Experience with modelgovernance, explainability, risk controls and responsible AI.
  

• Previous experience withinfinancial services, investment banking, payments or another highlyregulated environment.
  

Connect to your career at Deloitte

Deloitte drives progress. Using our vast range of expertise, we help our clients' become leaders wherever they choose to compete. To do this, we invest in outstanding people. We build teams of future thinkers, with diverse talents and backgrounds, and empower them all to reach for and achieve more.

What brings us all together at Deloitte? It’s how we approach the thousands of decisions we make every day. How we behave, our beliefs and our attitudes. In other words: our values. Whatever we do, wherever we are in the world, we lead the way, serve with integrity, take care of each other, foster inclusion, and collaborate for measurable impact. These five shared values lead every decision we make and action we take, guiding us to deliver impact how and where it matters most.