Senior ML Engineer

•    Implement audit trails, role-based access control, approval workflows and regulatory controls, and ensure AI/ML development and validation follow Responsible AI guidelines and standards.
•    Identify and escalate delivery and AI risks, define remediation roadmaps, and use guardrails to mitigate hallucination, unsafe actions, data leakage and compliance violations.

Model performance and continuous improvement
•    Monitor model and system performance after delivery, use relevant metrics to identify degradation or control gaps, and implement model improvements.
•    Use coding agents and AI-assisted SDLC practices, including GitHub Copilot, Claude Code, specification-driven development and open specification workflows, to improve delivery quality and consistency.

Technical Guidance and Collaboration 
•    Provide technical guidance and share practical engineering knowledge to improve team delivery and solution quality.
•    Coach and mentor colleagues on machine learning, agentic AI, harness engineering and AI-assisted SDLC practices.
•    Collaborate across disciplines, uphold strong risk and conduct standards, exercise sound technical judgment and contribute to an inclusive, accountable engineering culture.

Technical guidance, collaboration and mentoring
•    Provide technical direction, coaching and mentoring to colleagues building scalable machine learning and agentic AI solutions.
•    Work collaboratively across engineering, product, design, infrastructure and interfacing programme teams, and build effective stakeholder relationships.