Who are we?
At Finastra, we’re a global leader in financial services software, dedicated to expanding access to financial services and shaping what’s next for the industry. Our technology powers mission‑critical solutions across Lending, Payments and Universal Banking, supporting over 7,000 customers, including 80% of the world’s top 50 banks, in more than 110 countries.
Disclaimer:
Finastra has signed an agreement to sell its Universal Banking business to Pollen Street Capital, with completion expected end 2026 / early 2027, subject to consultation and customary closing conditions. Upon completion, this role is expected to transition to the standalone Universal Banking business, creating opportunities to support its future growth and success.
The opportunity
We are seeking a deeply technical Director of AI and Machine Learning to lead the engineering organisation’s AI capability. This role will translate product-led AI strategy into scalable architecture, engineering design and delivery, ensuring that AI can be consumed responsibly, efficiently and measurably across the software development lifecycle.
Acting as the technical authority for AI, machine learning and associated data considerations, the Director will guide technology choices, establish the AI engineering estate and help the organisation navigate a period of transition.
The role combines hands-on technical credibility with strategic leadership, cross-functional influence and a strong understanding of enterprise software engineering.
What you will do:
Own enterprise AI reference architecture , engineering patterns and technology standards along with the technical AI and machine learning strategy for Engineering, aligned with product priorities and the wider technology direction.
Establish reusable architectural patterns covering model access, orchestration, retrieval, data access, security, observability and integration with enterprise applications.
Define when teams should build, buy, reuse or consume AI capabilities and platforms.
Translate AI product ambitions into practical architecture, engineering designs, delivery patterns and measurable outcomes across the SDLC.
Establish and evolve the AI engineering estate, ensuring the organisation has the capabilities, platforms and operating model required to deliver effectively.
Assess AI technologies, models and tooling, monitor market developments and recommend the most appropriate options for enterprise use cases.
Define approaches for cloud-based MLOps, model deployment, monitoring, scalability and operational management.
Provide technical leadership on data strategy, including data inputs and outputs, data residency and the data foundations required for engineering-led AI use cases.
Advise on AI consumption economics and model-cost considerations, including usage-based and seat-based pricing structures.
Partner closely with Product, Architecture, Engineering and other senior stakeholders to ensure AI solutions deliver intended customer and business outcomes.
Build and lead a specialist capability, initially with a small number of direct reports and potentially adding machine-learning and data-science expertise as needs develop.
Create clear communication and governance across functions, bringing senior stakeholders with you through technical choices, trade-offs and delivery priorities
Lead the technical approach for Generative AI and agentic AI, including LLM selection, orchestration, RAG, tool use, agent design and evaluation.
Drive reusable platform capabilities for model access, prompt management, RAG, evaluation, observability, governance and cost management.
Prevent duplication by establishing common AI services and reusable engineering components across product teams.
What success looks like:
A coherent AI engineering estate and roadmap are established for the new organisation.
Engineering can reliably deliver the product AI strategy through scalable architecture, tooling and SDLC practices.
AI technology…