AI Agent Engineering Lead

We are seeking an AI Agent Engineering Lead to own the agent layer of the agentic framework, focusing on building and leading a team of agent engineers. The successful candidate will be responsible for leading key activities to progress their career, including owning the agent development pipeline, LLM integration, prompt-to-action engineering, and agent lifecycle.

The role involves building GenAI security use cases, such as alert triage automation and threat summarization, and developing reusable agent components that reduce effort on the next build. The ideal candidate will have 15+ years of experience in cybersecurity or AI engineering in a regulated environment, with a domain focus on AI Agent Engineering.

Key responsibilities:

• Own the agent layer: agent development pipeline, LLM integration, prompt-to-action engineering, and agent lifecycle
• Own agent evaluation and testing, including credible approaches to non-deterministic output
• Build GenAI security use cases, such as alert triage automation and threat summarization
• Develop reusable agent components that reduce effort on the next build
• Build to AI Architecture's specifications and raise design problems rather than engineering around them
• Apply binding AI security standards to every agent
• Prepare agent workstream handover packages to the standard the managed service needs
• Hold AI Platform Engineering to the model-serving endpoints, vector index service levels, and token budgets your agents depend on
• Lead the agent engineers building the agent layer and keep at least a third of your own week on code
• Run the two-week shadow period where the vendor's operators work your agents under supervision
• Mentor the agent engineers and set the engineering standard for the agent layer by example

Basic qualifications:

• 15+ years in cybersecurity or AI engineering in a regulated environment
• Domain focus: AI Agent Engineering (agent layer of the agentic framework)
• Hands-on leadership of an agent engineering team (build, not design)
• Agent engineering depth: LLM integration, prompt-to-action engineering, multi-agent orchestration, tool and function calling, memory and state management, RAG retrieval, prompt and model version control, and reusable agent components
• Agent safety engineering: evaluation harnesses for non-deterministic behavior, red-team testing, guardrails, human-in-the-loop checkpoints, containment, and rollback
• Building GenAI security use cases: alert triage automation, threat summarization, and automated investigation

Preferred qualifications:

• LangChain or LlamaIndex, vector databases, retrieval tuning, and production Python
• Agent observability, audit traceability, and guardrails
• AI red-teaming tools such as Garak or PyRIT
• Agent frameworks such as LangGraph, AutoGen, Semantic Kernel, or CrewAI
• Prompt and agent evaluation tooling: LangSmith, Promptfoo, or equivalent