• Lead a team of AI Engineers, ensuring alignment on goals, quality and delivery timelines. • Mentor and coach team members to support their technical and professional growth. • Drive engineering excellence by promoting best practices in coding (PEP-8, Clean Code), Test-Driven Development, code review, CI/CD and responsible AI development. • Plan and manage team capacity, sprints and milestones to ensure predictable delivery. • Own the design, evolution and operation of our RAG and agentic AI services — retrieval, embedding, indexing, orchestration, evaluation and LLM Ops. • Set and maintain standards for prompt engineering, evaluation harnesses, guardrails, hallucination mitigation and content safety. • Partner with the Data team to expose curated Druid datasets through natural-language interfaces (text-to-SQL / semantic search) and ensure quality and consistency end-to-end. • Collaborate with Product, UX and Architecture to translate customer and internal use cases into well-defined AI capabilities with clear acceptance criteria. • Champion responsible AI — bias awareness, transparency, privacy, data minimisation, evaluation, monitoring, and clear in-product communication of model limitations. • Collaborate with other Team Leaders, Development Managers, Architects and Product Owners to align engineering execution with business objectives. • Contribute to the evolution of development processes, CI/CD pipelines and DevOps practices. • Foster a culture of continuous improvement, innovation and knowledge sharing — including a research-aware approach to a fast-moving field.

• Own outcomes end-to-end — not tasks, not tickets. You pick a problem, architect the AI-powered solution, ship it, and measure the impact yourself. • Build agentic systems — design autonomous loops that reason, act, and iterate. Think agents that handle workflows, not just answer questions. • Deploy autonomous pipelines end-to-end — no human in the middle. From trigger to result, your systems handle the reasoning, the actions, and the handoffs. You measure output, not effort. • Operate as a one-person company — scope, prioritize, execute, and iterate with radical ownership. AI is your team. • Cross-pollinate across CloudWalk — what you build in your domain becomes a blueprint for every other team.

• Explore and build proof-of-concept AI agents to automate complex internal engineering workflows, such as incident analysis or code review summaries. • Develop and optimize Retrieval-Augmented Generation (RAG) pipelines to enable "chat with your data" capabilities for internal technical documentation and runbooks. • Collaborate with cross-functional teams to identify operational bottlenecks and implement AI-driven fixes that improve efficiency and performance. • Test and document the use of emerging AI-assisted coding and monitoring tools to determine their viability for the broader engineering organization. • Participate in security-minded engineering, ensuring that automated workflows follow strict data hygiene and secrets management protocols.

• Lead the execution and coordination of the organisation’s AI Transformation Plan across the business. • Ensure transformation activities remain aligned with agreed maturity assessments, portfolio objectives, and business priorities. • Coordinate AI transformation activities across departments to identify practical opportunities for AI-enabled improvement and operational efficiency. • Maintain tracking and reporting of AI initiatives, including delivery momentum, risks, dependencies, and blockers, escalating issues where required. • Engage with and support portfolio-level AI transformation activities, including AI assessments, reporting, progress reviews, forums, peer groups, and related transformation initiatives as required. • Support staff and leaders through the transition to AI-enabled ways of working by providing practical education, training, guidance, and hands-on support. • Work with teams across R&D, product, sales and marketing, customer care, and operations to identify, trial, adopt, and embed practical AI tools and workflows that improve efficiency, quality, delivery, and operational effectiveness. • Establish practical AI working standards, governance expectations, and usage guidance to support safe, consistent, and effective adoption across the business. • Ensure appropriate documentation, controls, reporting, and oversight are maintained for AI-related initiatives and activities. • Reinforce responsible AI usage and ensure AI practices remain aligned with organisational security, compliance, risk management, and ethical expectations.