Director, Risk & Compliance Engineering & Assurance
On-siteLondon, England, United Kingdom
Job Summary
Set the technical strategy for Risk & Compliance AI and automation, owning the roadmap for agentic workflows and data products. Build, publish, and continuously improve governed risk, compliance, privacy, and resilience data products while modernizing third-party due diligence automation. Lead the use of technology to mature automated assurance work, moving from manual testing to continuous, evidence-led controls testing. Build and develop a high-performing team, defining roles, skills, and operating models to deliver at pace. Engineer for trust by ensuring every solution aligns with Responsible AI, data governance, and fiduciary-grade standards. This role requires 12+ years of engineering leadership experience with a focus on AI/ML, Python, and cloud-native platforms. The position is a Director-level role with hybrid work flexibility and competitive benefits including mental health days and tuition reimbursement.
Required Qualifications
- Engineering and AI leadership
- Building teams and shipping at scale
- Leading technical teams that deliver production-grade software, data, and AI
- Setting direction in ambiguity
- Translating complex business problems into scalable, well-governed solutions
- Making architecture and build-versus-buy decisions
- Coaching teams
- Shaping roadmaps with senior stakeholders
- Holding delivery accountable
- Substantial experience leading software engineering, data, or AI/ML teams
- 12+ years of relevant experience
- Several years in a leadership capacity
- Delivering production-grade solutions at enterprise scale
- Strong hands-on foundations in modern data and AI engineering
- Python
- SQL
- Cloud-native platforms
- Data pipelines
- Demonstrated experience with AI/ML technologies
- LLMs
- Retrieval Augmented Generation (RAG)
- Agentic workflows
- Natural Language Processing (NLP)
- Predictive analytics
- Discipline to run AI/ML reliably in production
- Experience building on shared enterprise data platforms
- Snowflake
- Modern interfaces such as MCP
- Proven ability to build, grow, and retain a high-performing team
- Defining roles, skills, and competencies
- Developing existing talent into a world-class capability
- Experience designing the operating model of a technical team
- Designing ways of working
- Delivery cadence
- Quality standards
- Tooling
- In a product operating model context
- A coaching leadership style
- Experience automating risk, compliance, or assurance processes with technology and AI
- Third-party or customer due diligence
- Monitoring
- Controls and controls testing
- Enterprise control testing
- Understanding of how to embed risk identification, control design, and issue lifecycle management into technology solutions
- Understanding of Responsible AI practices
- Model governance
- Data access control
- Privacy-by-design
- Ability to hold solutions to TR's standards
- Ability to determine and implement how AI subscribes to sensitive signals
- Assuring the reliability of AI-generated insight
- Monitoring AI reliability
- Drift detection
- Graceful handling of failed data feeds
- Strong communication skills
- Ability to explain technical and AI concepts to non-technical senior audiences
- Experience partnering with senior business and technology stakeholders
- Translating needs into products
- Influencing decisions through data, insight, and clear storytelling
- Commercial judgement
- Bias to enable
- Framing risk so leaders can act
- Holding owners accountable across functions the team does not control
- Finding the safe path to yes
- Proven engineering leadership
- Strong AI and data foundations
- Judgement to embed sound risk and control principles into what you build
- Lead with a bias to enable
- Finding the safe, fast path to yes
- Treat the function as a catalyst for accountable growth
- Set and own the Risk & Compliance AI and data-product strategy and roadmap
- Lead the forward-engineering central team
- Hiring
- Capability development
- Establish the rent-not-build partnership with the Data & Analytics organisation
- Shared-service model
- Deliver priority data products and automation
- Own the data-product operating disciplines
- Product management
- Semantic and taxonomy ownership
- AI access and insight assurance
- Measure and report success by decision-influence and accountability outcomes
- VP Risk
- Risk partnering directors across Enterprise Risk & Policy Governance
- GTM
- Operations
- Technology & Data
- TPRM
- Assurance & M&A
- Compliance
- Privacy
- Resilience
- Legal
- Data & Analytics
- CTO
- Product
- CIO
- CDO organisations
- Internal Audit
- LLMs and agentic frameworks
- RAG architectures
- AI agents and orchestration
- LangChain / LangGraph
- Python and modern data engineering
- Snowflake and governed semantic layers
- AWS / GCP cloud services
- Vector databases
- Data pipelines and MLOps tooling
- Business intelligence and visualisation tools
Desired Qualifications
- Deep risk, compliance, or audit domain knowledge
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