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About the role
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.
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