GenAI Tech Senior Lead

About the role

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. This role centers on architecting and delivering solutions built on pre-trained and hosted foundation models, not on training or fine-tuning models. Hybrid working model with 3 days in the office and 2 days working remotely, based in Tampa, FL (with collaboration across Irving, TX).

Responsibilities

• 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.
• Build, mentor, and manage a high-performing team of AI engineers and specialists; foster a culture of execution, collaboration, and continuous improvement.
• Oversee the design, development, and deployment of robust, scalable, and production-ready GenAI and agentic applications; ensure solutions meet rigorous performance, security, and quality standards.
• 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.
• Serve as the primary point of contact for GenAI delivery; manage stakeholder expectations, communicate project progress, identify and mitigate risks, ensure on-time and on-budget delivery.
• Collaborate closely with Data Mesh, Cloud Architecture, MLOps/LLMOps, and business unit teams for seamless integration and operationalization of GenAI and agentic solutions.
• Drive adoption of best practices in software development (CI/CD), LLMOps, agent observability, and project management (Agile/Scrum) within the AI team.
• 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.