Technical Director Core AI Engineering

• 12+ years of engineering, data, platform, or technology-delivery experience, including significant leadership responsibility in complex enterprise environments.
• Demonstrated experience designing and validating enterprise AI, generative AI, or intelligent-automation capabilities through working implementation and technical handoff.
• Strong experience in enterprise architecture, systems integration, cloud/data/application platforms, and secure engineering practices.
• Practical experience with AI orchestration, evaluation, observability, security, and adoption practices.
• Ability to lead distributed technical teams and establish scalable engineering, DevOps, SRE, and quality practices.
• Strong technical communication and decision-making skills.

• Experience creating reusable AI reference architectures and standards.
• Knowledge of vector databases, embeddings, hybrid retrieval, agentic workflows, structured tool calling, context management, model serving, evaluation, and observability.
• Experience in a global consumer-goods organization or similarly complex distributed enterprise.

This is a hands-on director-level technical leadership role with a clear mission: identify, design, and validate the next enterprise AI capabilities with the greatest potential to create business value.

The Director of Core AI Engineering will set the technical direction and operating model for emerging and complex enterprise AI capabilities, combining accountable technical leadership with direct involvement in architecture, critical engineering decisions, prototypes, enterprise integrations, code and design reviews, and technical validation.

You will lead a small, highly capable team and partner closely with AI Product Delivery, data, security, infrastructure, and application-engineering leaders to prove what is technically possible, resolve key architectural and integration questions, and create the technical blueprint and evidence needed for capabilities to be industrialized by delivery teams.

• Own the technical exploration roadmap, target architecture, and engineering standards for emerging enterprise AI capabilities.
• Define architectural boundaries, design assumptions, and technical decision criteria in partnership with Product Delivery, data, security, infrastructure, and engineering teams.
• Make pragmatic decisions on architecture, model access and routing, orchestration, retrieval, integrations, and build-versus-buy choices.
• Create reusable reference patterns for AI orchestration, enterprise integrations, evaluation, observability, security, context management, and LLMOps.
• Architect and validate agent runtimes, including single- and multi-agent orchestration, workflow routing, human-in-the-loop controls, retries, fallbacks, and recovery mechanisms.
• Design and test RAG and enterprise retrieval approaches, including ingestion, embeddings, vector indexing, hybrid retrieval, access controls, reranking, and knowledge-graph approaches.
• Prototype context-engineering, caching, memory, tool-using AI systems, and closed-loop improvement mechanisms.
• Establish viable model-serving and inference designs, including gateways, routing, latency, scalability, cost, performance, and fallback requirements.
• Select and test fit-for-purpose technologies based on measurable technical evidence.
• Lead prioritized use cases through discovery, prototype, enterprise integration, evaluation, security review, and technical handoff.
• Remain hands-on in architecture, design and code reviews, critical prototypes, technical problem solving, and readiness decisions.
• Define reference implementations, interfaces, quality evidence, non-functional requirements, limitations, and acceptance criteria for industrialization.
• Establish clear handoff and knowledge-transfer points with delivery, platform, security, and operations teams.
• Establish measurable evaluation approaches covering quality, groundedness, retrieval performance, tool reliability, safety, latency, cost, and regression detection.
• Instrument prototypes to capture system behaviour, usage, failures, traces, model and prompt versions, latency, cost, and user feedback.
• Partner with security and data-governance teams to implement appropriate controls for sensitive data, AI security risks, access, and unreliable outputs.
• Balance ambitious experimentation with disciplined engineering practices.
• Lead, mentor, and develop senior AI and platform engineers, setting priorities and technical quality standards.
• Drive architecture reviews and technical decision-making across the AI portfolio.
• Champion technical learning, reference architectures, and standards that enable delivery teams to scale proven capabilities.