Lead AI/ML Solutions

Role overview

Engineering and delivery

• Set engineering direction and guide orchestration, tool calling, retrieval, memory, workflow automation, enterprise integration, human-in-the-loop controls, and sandboxed execution.
• Own delivery from intake and discovery through design, build, test, release, adoption, support, and continuous improvement.
• Partner with business, product, risk, compliance, and technology stakeholders on priorities, outcomes, dependencies, controls, and adoption.
• Establish standards for code quality, reviews, documentation, testing, model evaluation, release controls, traceability, and evidence.
• Ensure readiness across security, reliability, scalability, resilience, observability, telemetry, incident response, rollback, and handover.

Governance and team leadership

• Identify, assess, mitigate, and escalate delivery, technology, security, data, model, operational, and regulatory risks.
• Embed responsible AI, model risk, data protection, access control, approval, auditability, and governance controls.
• Monitor adoption, performance, evaluation, control effectiveness, incidents, and business outcomes; drive remediation and evolution.
• Build reusable engineering patterns, platforms, and AI-assisted SDLC accelerators.
• Lead and develop engineers through direction-setting, work allocation, coaching, hiring, onboarding, mentoring, and career development.

Experience and qualifications

• 8–12 years of relevant hands-on experience, including engineering leadership and enterprise AI/ML or agentic AI delivery in regulated environments.
• Master's degree with a specialization in Technology.
• English language.

Must-have skills

• Agentic AI solution architecture and engineering design.
• Engineering leadership, team development, and delivery accountability.
• Enterprise software engineering, platform integration, and AI-assisted SDLC.
• Responsible AI, model risk, data protection, control governance, production readiness, observability, resilience, and operational support.
• AI/ML delivery lifecycle, evaluation, and continuous improvement.

Work arrangements

• Flexible working options based around home and office locations, with flexible working patterns.

Benefits

• Benefits include retirement savings, medical and life insurance, flexible benefits in some locations, leave, wellbeing support, and learning opportunities.