Head of Data & AI - UK Based

AI Product Delivery & Strategy 

• Own the end-to-end technical strategy and execution roadmap for AI-enabled product capabilities across the StarCompliance platform. 

• Drive adoption of generative AI, LLM-based architectures, predictive analytics, graph intelligence, recommendation systems, and semantic search where these deliver measurable customer value. 

Data Platform & Engineering 

• Own the data foundation strategy, ensuring clean, trusted, well-governed data underpins every AI initiative. 

• Build robust MLOps capabilities, including model training pipelines, versioning, monitoring, A/B experimentation, and drift detection. 

Internal AI Enablement 

• Champion pragmatic AI adoption within engineering and product development, accelerating how we build, not just what we build. 

Leadership & Team Building 

• Build, mentor, and scale a high-performing Data & AI organisation across data engineering, data science, ML engineering and analytics. 

Cross-Functional & Executive Collaboration 

• Work closely with the CTO and executive team to align AI and data initiatives with company strategy and commercial priorities. 

• Partner with the Product Director, AI & Data Products, co-owning the AI roadmap, prioritisation, and delivery outcomes. 

AI & Machine Learning 

• Proven delivery of production-grade AI and ML systems at scale, not just experimentation. 

• Deep experience with generative AI and LLM-based application architectures (fine-tuning, prompt engineering, RAG, agentic frameworks). 

• Strong knowledge of vector databases and semantic search (e.g. Pinecone, Weaviate, pgvector, Azure AI Search). 

Data Engineering & Platforms 

• Deep background in modern data engineering, ELT/ETL patterns, and large-scale data pipeline architectures. 

• Hands-on experience with Snowflake (or equivalent cloud data warehouse) and associated data modelling patterns. 

• Strong Azure ecosystem experience: Azure Data Factory, Azure Synapse, Azure OpenAI Service, and Azure Machine Learning. 

Software Engineering & Architecture 

• Strong software engineering fundamentals and the credibility to engage at technical depth with senior engineers. 

• Experience with cloud-native, microservices, and event-driven architectures on Azure (or equivalent hyperscaler). 

• Ability to make sound build vs buy vs integrate decisions across the AI and data tooling landscape. 

Leadership & Business 

• Proven experience building and leading high-performing Data / AI engineering teams. 

• Experience within financial services, regtech, compliance, surveillance, or similarly regulated domains. 

• Strong commercial instincts — the ability to connect technical investment to customer value and business outcomes.