Lead AI Solutions Architecture & Engineering

Role Purpose

Lead the technical design and engineering quality of AI solutions built by the Digital & AI Centre of Excellence. The role is expected to provide practical architecture direction, guide junior AI engineers, and ensure that solutions are reliable, maintainable, secure and aligned to business needs.

This is a hands-on technical leadership role that should bring stronger engineering discipline into the AI COE while working closely with product, stakeholder-engagement and governance leads.

Core Expectations

1. Own AI solution architecture and engineering quality

• Design practical AI solution architectures covering data flow, model usage, integrations, security, deployment readiness and supportability.
• Review technical designs, code quality, testing coverage and implementation choices before solutions move into production or wider use.
• Guide the team on engineering practices such as documentation, version control (code, prompts etc.), code reviews, reusable components and release discipline.

2. Build reusable AI capability for the COE

• Create reusable patterns, templates and accelerators for common AI use cases such as knowledge retrieval, document intelligence, workflow automation and AI-assisted decision support.
• Define simple and practical standards for prompt management, model evaluation, retrieval pipelines, monitoring and failure handling.
• Help the COE move from ad hoc implementation towards an expertise, enablement, monitoring and governance-led operating model.

3. Mentor AI engineers and support business-line AI PODs

• Provide day-to-day technical direction to junior AI / ML engineers and help them mature into stronger solution owners.
• Work with product and stakeholder-engagement leads to convert business requirements into technically sound, usable solutions.
• Support business-line AI PODs with technical guidance, review support and reusable implementation approaches.

Required Experience

• 7-10 years of experience in software engineering, solution architecture, data engineering, machine learning engineering or AI application development.
• Hands-on experience building or technically leading AI / ML / GenAI solutions in an enterprise environment.
• Strong experience in Python and modern software engineering practices.
• Experience guiding junior engineers, reviewing code and setting technical direction for a small engineering team.
• Experience in financial services, wealth management, capital markets, asset management or another regulated industry will be preferred.

Required Skills

• Strong technical problem-solving and architecture thinking.
• Practical understanding of LLM-based applications, retrieval-augmented generation, embeddings, APIs, model integrations and AI application design.
• Ability to balance speed of experimentation with engineering quality, security, maintainability and control requirements.
• Ability to communicate technical decisions clearly to both engineers and non-technical stakeholders.
• Clear ownership mindset with the ability to mentor, guide and improve the overall engineering maturity of the AI COE.

Role Success Measures

• Improved reliability, maintainability and architecture quality of AI solutions delivered by the COE.
• Clear engineering standards and reusable patterns adopted by the AI engineering team.
• Reduced dependency on ad hoc technical decisions by junior engineers.
• Stronger technical enablement of business-line AI PODs.
• Progressive maturity of junior AI engineers into independent solution owners.