Lead Agentic AI Engineer

## Lead Agentic AI Engineer Apply:
Hybrid:
Irving Texas United States:
Tampa Florida United States:
Full time:
Posted Today:

** Job Overview
** We are seeking an experienced Lead Agentic AI Engineer to help drive the design, development, and integration of state-of-the-art Generative AI and
** agentic AI
** solutions across our enterprise Controls Technology platform. You will collaborate with cross-functional teams, contribute deep technical expertise in context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration, and play a key role in delivering scalable, grounded AI solutions to enhance automation and operational efficiency. This role centers on architecting robust applications and agent systems on top of pre-trained and hosted foundation models — not on training or fine-tuning models.
** Key Responsibilities
*** Collaborate with AI architects, leads, and stakeholders to design and implement generative and agentic AI solutions that address business challenges.
* Architect advanced
** context engineering
** strategies — context layering, chaining, compression, pruning/offloading, and memory management — to maximize reliability, provenance, and token efficiency in production.
* Design and implement advanced generative AI methods, including sophisticated
** prompt engineering
** and
** Retrieval-Augmented Generation (RAG)**.
* Build and optimize
** RAG systems**, including hybrid search, multi-vector retrieval, and re-ranking pipelines.
* Design and implement
** knowledge graphs
** and
** Graph RAG
** architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.
* Architect
** agentic workflows and multi-agent systems
** using
** Google Agent Development Kit (ADK)
** and comparable frameworks (Lang Graph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.
* Design robust
** agent harnesses** — governance, constraints, feedback loops, state/session management, and execution controls that make long-running agent systems reliable and safe.
* Integrate agents with tools and data via the
** Model Context Protocol (MCP)
** and orchestrate inter-agent collaboration and task delegation via the
** Agent2

Agent (A2A)
** protocol.
* Support the integration of GenAI and agentic applications into production environments, ensuring robust deployment, scalability, observability, and maintainability.
* Contribute to the development and optimization of real-time and streaming AI solutions.
* Stay current with the latest advances in generative and agentic AI and actively share knowledge with the team.
* Ensure adherence to ethical AI guidelines, guardrails, agent isolation/sandboxing, data privacy, and compliance standards.
* Mentor junior team members, provide code reviews, and foster a culture of technical excellence.
** Required Technical Skills
*** Deep, hands-on expertise in
** core generative AI concepts** — foundation models, LLMs, embeddings, tokenization, and context-window management.
* Advanced skills in
** prompt engineering
** and
** context engineering**, including familiarity with prompt design tools/frameworks and dynamic context orchestration.
* Strong experience building
** RAG systems**, including chunking strategies, hybrid search, and multi-vector retrieval.
* Practical experience designing
** knowledge graphs
** and
** Graph RAG
** pipelines (e.g., using graph databases such as Neo4j or ArangoDB) for relationship-aware, multi-hop retrieval.
* Proven experience building
** agentic AI systems
** with
** Google ADK
** and/or comparable frameworks (Lang Graph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), including tool/function calling,…