Role summary
The leader will coach and grow AI engineers, shape technical strategy, and directly guide the design and delivery of agentic solutions that interact with enterprise systems and operate within clear governance, safety and security boundaries. The role is accountable for rapid experimentation, evaluation, and rapid prototyping, while also ensuring that successful patterns can be responsibly reused and scaled across enterprise.
Key responsibilities
- Lead, mentor and develop a team of AI/ML engineers; set technical direction, create development plans, manage capacity, provide coaching and build an inclusive, high-performing engineering culture
- Partner cross-divisionally with business, product, architecture, data, cybersecurity, privacy, legal, risk and operations stakeholders to identify, prioritize and define business problems suitable for AI, GenAI and Agentic AI solutions
- Architect and build agentic experiences using enterprise-ready patterns such as single-agent patterns, multi-agent orchestration, tool/function calling, RAG, memory, policy-aware context management, human-in-the-loop escalation and reusable agent skills/workflows
- Evaluate and apply modern AI engineering tools and Agentic AI IDEs/coding assistants to accelerate design, prototyping, testing and delivery while maintaining quality, security and compliance expectations
- Drive hands-on prototypes, pilots and proofs of concepts that validate business value, user experience, technical feasibility, cost profile, safety posture and operational readiness
- Establish reusable AI assets, including prompts, evaluation datasets, guardrail patterns, MCP-enabled tool integrations, RAG pipelines, observability dashboards, reference architectures and implementation playbooks
- Define and influence technical standards for agent development
- Embed responsible AI principles, data privacy, model risk awareness, explainability, human oversight, secure-by-design practices and enterprise governance controls into every stage of experimentation and delivery
- Communicate findings clearly to senior leaders and enterprise partners through demos, technical readouts, decision memos, scorecards and adoption recommendations that help inform broader scale decisions
- Stay current on emerging GenAI, Agentic AI, ML platforms, model capabilities, orchestration frameworks, governance practices and AI safety/security threats; translate market learning into pragmatic recommendations
- Strong foundation in Machine Learning concepts and techniques, including model development, training, evaluation and optimization, with practical experience in areas such as classification, regression, NLP, computer vision, forecasting or recommendation systems
