AI/ML Engineer - Financial Crime - #2176664

Location: Sheffield (hybrid)

Office requirement: 1 day per week on-site

Domain: Financial Crime Risk (AML / sanctions / fraud / investigations)

Contract: InsideIR35

What you’ll do

• Deliver production-grade AI and Agentic AI solutions for financial crime risk detection and investigation.
• Work across engineering, ML engineering/data science, product, operations, compliance, model risk, architecture, cyber security, and technology controls.
• Assess data landscapes and business problems to determine appropriate machine-learning approaches, selecting, testing and evaluating models based on the specific use case.
• Contribute throughout build, testing and deployment, working with engineering teams to support the transition of solutions into production.

Hands-on technical responsibilities:

• Contribute to solution design and build, including:

-Machine-learning model selection, experimentation, testing and evaluation.

-LLM/agent workflow design (tool use, orchestration, guardrails, human-in-the-loop patterns).

-API/service integration into alerting, case management, and investigator workflows.

-Evaluation harnesses (golden datasets, regression tests, red-teaming scenarios, offline metrics).

• Apply technical judgement to determine which modelling approaches are appropriate based on the characteristics of the data, financial crime domain and business problem.
• Contribute to technical and design reviews and work with engineering teams to ensure solutions are suitable for production.

What you’ll bring (essential)

• Proven AI/ML engineering delivery experience (8-10 years) for complex technology initiatives in regulated environments.
• Hands-on experience delivering AI solutions to production (GenAI/LLM and/or agentic patterns) with measurable operational outcomes.
• Financial Crime Risk domain experience (AML/sanctions/fraud/investigations) with evidence of operational adoption

Strong technical grounding in several of:

• Python (and/or JVM languages), REST/gRPC APIs, microservices
• Containers/Kubernetes (basic), CI/CD
• SQL and data integration patterns (batch/stream), data quality controls
• LLM/agent patterns: prompt/version management, tool/function calling, RAG, safety guardrails
• Experience in AI evaluation and monitoring (offline test sets, regression testing, drift/performance monitoring, human feedback loops).