Required Experience

- Strong hands-on experience building production-grade LLM, agentic AI, ML, automation, or platform systems.

- Deep understanding of agent architecture, orchestration frameworks, tool calling, memory design, RAG, model routing, and multi-agent workflows.

- Experience with frontier models, open-source models or both, including evaluation, benchmarking, and model comparison.

- Strong software engineering background, including Python, APIs, backend services, cloud platforms, containers, CI/CD, authentication, logging, and production observability.

- Experience integrating AI systems with enterprise APIs, identity systems, data platforms, workflow engines, ticketing systems, code repositories and operational tools.

- Prior experience operating or supporting production systems, including monitoring, alerting, incident response, rollback, release management, access control, cost management, and post-incident review.

- Practical understanding of production failure modes such as model drift, prompt regressions, broken tool calls, API failures, retrieval errors, permission issues, latency problems, data quality gaps, cost spikes, and unsafe outputs.

- Practical understanding of AI safety risks, including hallucination, prompt injection, insecure tool use, excessive agency, sensitive data leakage, memory poisoning, adversarial manipulation, and unsafe autonomous behavior.

- Experience designing human-in-the-loop workflows for high-risk, regulated or security-sensitive environments.

- Ability to design for operational handover, including runbooks, support models, service ownership, observability, change control and measurable service health.

Preferred Experience

- Experience building AI agents for software engineering, code review, test generation, vulnerability discovery, workflow automation or enterprise operations.

- Experience with Lang Graph, Auto Gen, Crew AI, Semantic Kernel, Agent Sea, Open AI Agents SDK, MCP, vector databases, graph databases or similar agentic AI tooling.

- Experience with RAG pipelines, knowledge graphs, structured retrieval, event schemas, data contracts and context engineering.

- Experience with secure connector patterns, permission boundaries, service accounts, API gateways, immutable audit logging and tool mediation.

- Experience with AI red teaming, model evaluation, AI governance, secure-by-design AI or regulated-sector AI deployment.

- Experience designing or operating simulation environments, cyber ranges, replay systems, benchmark suites or adversarial test harnesses.

- Exposure to cybersecurity, AppSec, cloud security, DevSecOps, vulnerability management, SOC operations, incident response, threat intelligence, GRC or offensive security testing.

Principal AI Engineer