Director, AI & Machine Learning

In this role you will lead the AI and ML capabilities for Finastra, translating product-led AI strategy into scalable architecture and delivery. You will act as the technical authority on AI, ML, and data considerations, guiding architecture, platforms, and governance through a period of organizational transition. You will work closely with Product, Architecture and Engineering to deliver measurable AI outcomes across the SDLC, balancing innovation with enterprise needs. This is a hands-on, leadership-focused position that shapes how AI enables next-generation financial software.

• unlimited vacation
• hybrid working
• wellbeing programs
• medical, life and disability insurance
• retirement plans
• volunteering time off and donation-matching

• Own enterprise AI reference architecture, engineering patterns and technology standards aligned with product priorities
• Establish reusable architectural patterns for model access, orchestration, data access, security, observability and integration
• Define when to build, buy, reuse or consume AI capabilities and platforms
• Translate AI ambitions into practical architecture, designs, delivery patterns and measurable SDLC outcomes
• Evolve the AI engineering estate with required platforms and operating model
• Assess AI technologies, models and tooling; monitor market trends and recommend options
• Define cloud-based MLOps, model deployment, monitoring, scalability and operational management
• Lead data strategy, including data inputs/outputs, residency and governance foundations
• Advise on AI consumption economics and licensing/cost structures
• Partner with Product, Architecture, Engineering and executives to ensure AI delivers customer and business outcomes
• Build and lead a specialist capability with potential expansion of ML and data-science skills
• Communicate governance and technical trade-offs across functions and stakeholders
• Lead the technical approach for Generative AI and agentic AI (LLM selection, orchestration, RAG, tool use, evaluation)
• Develop reusable platform capabilities for model access, prompt management, RAG, evaluation and cost management
• Prevent duplication by creating common AI services and reusable components across product teams

• Extensive software engineering, architecture and enterprise SDLC experience
• Around 4–5 years applying AI/ML in engineering or enterprise software
• Deep knowledge of AI/ML tech, models, tooling trends and practical trade-offs
• Strong architectural capability to shape complex enterprise applications
• Hands-on experience with cloud MLOps and deployment/operation of ML models in cloud environments
• Understanding of data strategy and governance, data flows and residency
• Experience evaluating AI model and service costs, including consumption/licensing options
• Ability to engage credibly with engineers, architects and senior stakeholders
• Proven cross-functional leadership and stakeholder management across Product, Engineering and executives
• Experience building or developing specialist technical teams in changing organizations
• Desirable: experience in financial services/fintech or core banking platforms
• Desirable: security, resilience, regulatory and cost-control considerations in design

• Strategic thinking
• Cross-functional collaboration
• Communication of complex technical decisions
• AI/ML technologies and model options
• Enterprise AI tooling and platforms
• Cloud MLOps and model deployment