MLOps Engineer
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Summary
Contract MLOps Engineer at Deloitte, embedded with a global financial services client in London (hybrid, 3-4 days on-site). Builds production-ready AI agents, RAG systems and scalable ML pipelines using Python, Databricks, LLMs and AWS/Azure, with AI evaluation, monitoring and secure enterprise deployment.
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
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.
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.
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.
Skills
- A/B Testing
- Agentic AI
- Agile
- AI
- API
- Automation
- AWS
- Azure
- Bitbucket
- CI/CD
- Cloud
- Confluence
- Containerization
- Data Engineering
- Databricks
- Embeddings
- Feature Engineering
- Generative AI
- Kubernetes
- LLM
- Machine Learning
- MCP
- MLOps
- Model Evaluation
- Observability
- Python
- RAG
- Snowflake
- Splunk
- Test Automation
- Vector Databases
- Vector Search
