Generative AI Engineering Lead

We are seeking a results-driven Generative AI practitioner with end-to-end experience for the execution and deployment of cutting-edge Generative AI and agentic AI solutions across our enterprise-wide Controls Technology platform. In this role, you will be responsible for translating AI strategy into tangible, production-ready capabilities that enhance operational efficiencies and drive business value. We're looking for someone who combines deep technical expertise in generative AI — including context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration — with a proven track record of successfully delivering complex technology projects. This role centers on architecting and delivering solutions built on pre-trained and hosted foundation models, not on training or fine-tuning models.

GenAI Delivery Leadership: Execute the delivery roadmap for generative and agentic AI projects, ensuring alignment with business objectives and timelines. Manage the project lifecycle from ideation and scoping to deployment and post-launch support.

Leadership & Mentorship: Build, mentor, and manage a high-performing team of AI engineers and specialists. Foster a culture of execution, collaboration, and continuous improvement to successfully deliver on the AI roadmap. End-to-End Solution Delivery: Oversee the design, development, and deployment of robust, scalable, and production-ready GenAI and agentic applications. Ensure all solutions meet rigorous performance, security, and quality standards before and after deployment. Agentic Solution Delivery: Drive the design and delivery of agentic workflows and multi-agent systems , establishing standards for agent harnesses , orchestration patterns, and reliable long-running agent execution across the platform. Stakeholder & Program Management: Serve as the primary point of contact for GenAI delivery. Manage stakeholder expectations, communicate project progress, identify and mitigate risks, and ensure on-time and on-budget delivery. Cross-Functional Partnership: Collaborate closely with Data Mesh, Cloud Architecture, MLOps/LLMOps, and business unit teams to ensure the seamless integration and operationalization of GenAI and agentic solutions into our existing technology ecosystem. Technical Excellence & Best Practices: Drive the adoption of best practices in software development (CI/CD), LLMOps, agent observability, and project management (Agile/Scrum) within the AI team to ensure efficient and repeatable delivery. Governance & Ethical Deployment: Implement and enforce robust governance and ethical AI frameworks throughout the delivery process — including guardrails, agent isolation/sandboxing, and responsible AI practices — ensuring compliance with data privacy standards and corporate policies. Required Technical Skills Core Generative AI Concepts & Programming language: Deep understanding of foundation models, LLMs, embeddings, tokenization, and context-window management. Fluent in applying pre-trained and hosted models to enterprise use cases. Hands on experience in Python. Context Engineering: Expertise in advanced context engineering — context layering, chaining, compression, pruning/offloading, and memory management — to maximize reliability, provenance, and token efficiency in production. Prompt Engineering: Adept at advanced prompt engineering techniques and best practices, with familiarity with frameworks that facilitate effective prompt design and management. Retrieval-Augmented Generation (RAG): Advanced knowledge of RAG techniques, including hybrid search, multi-vector retrieval, Hypothetical Document Embeddings (HyDE), self-querying, query expansion, re-ranking, and relevance filtering. Knowledge Graphs & Graph RAG: Experience designing and delivering knowledge graphs (e.g., using graph databases such as Neo4j or ArangoDB) and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded