Director, AI & Machine Learning

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.




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.

• 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.
• 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 and model choices are grounded in technical fit, operational requirements, data considerations and total cost of consumption.
• Product and Engineering operate with clear alignment on priorities, accountabilities and customer impact.
• A strong specialist team and partner network are developed around AI, machine learning and data science.

• Highly experienced across software engineering, architecture and enterprise SDLC environments.
• Approximately four to five years of substantive experience applying AI and machine learning in an engineering or enterprise software context.
• Deep, current knowledge of AI and machine-learning technologies, model options, tooling trends and their practical trade-offs.
• Strong architectural capability, with experience shaping engineering design for complex enterprise applications.
• Practical experience with cloud MLOps and the deployment and operation of machine-learning models in cloud environments.
• Knowledge of data strategy and governance considerations, including data flows and data residency.
• Experience evaluating the commercial and technical cost of AI models and services, including different consumption and licensing approaches.
• The technical depth to engage credibly with engineers and architects, combined with the ability to articulate strategy to senior stakeholders.
• Demonstrated cross-functional leadership and stakeholder management across Product, Engineering and executive groups.
• Experience building or developing specialist technical teams and creating capability in a changing organisation.
• Experience in financial services, fintech, banking technology or core banking platforms.
• Experience delivering AI capabilities within a structured, enterprise-scale software development environment.
• Exposure to customer-facing enterprise applications where security, resilience, regulatory requirements and cost control are important design considerations.

Leadership profile

You are an engineering-led AI leader rather than a purely product or research-focused specialist. You combine technical authority with sound judgement, can move comfortably between strategy and implementation detail, and communicate complex decisions with clarity. You build trust across organisational boundaries, challenge constructively and create the conditions for teams to turn emerging AI capabilities into dependable enterprise solutions.

We are proud to offer a range of incentives to our employees worldwide. These benefits are available to everyone, regardless of grade, and reflect the values we stand for:

Flexibility: Enjoy unlimited vacation, subject to local regulations and business priorities. Benefit from hybrid working arrangements and inclusive policies such as paid time off for voting, bereavement, and sick leave.

Well‑being: Access confidential one‑to‑one support through our Employee Assistance Program, connect with our network of Wellbeing Champions and Gather Groups, and take part in monthly events and initiatives designed to help you thrive—inside and outside of work.

Health & Financial Security: Medical, life and disability insurance, retirement plans, lifestyle, and other benefits.*

Sustainability: Paid time off for volunteering and donation‑matching opportunities to support causes that matter to you.

Inclusion: Get involved in our inclusion communities, such as Count Me In, Culture@Finastra, Proud@Finastra, Disabilities@Finastra, and Women@Finastra—open to everyone who wants to participate and contribute.

Career Development: Access online learning and accredited courses through our Skills & Career Navigator tool.

Recognition: Take part in our global recognition program, Finastra Celebrates, and share your voice through regular employee surveys that help shape our culture and ways of working.

• Specific benefits may vary by location.

At Finastra, each individual is unique—bringing their own ideas, perspectives, cultural backgrounds, and experiences. We learn from one another, value what makes us different, and create an environment where everyone feels included, supported, and able to be their authentic selves.

Be unique. Be exceptional. Help us make a difference at Finastra.