Principal AI Engineer

Required Experience

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

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

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

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

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

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

- Practical understanding of production failure modessuch as model drift, prompt regressions, broken tool calls, API failures,retrieval errors, permission issues, latency problems, data quality gaps, costspikes, 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 unsafeautonomous behavior.

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

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

Preferred Experience

- Experience building AI agents for softwareengineering, code review, test generation, vulnerability discovery, workflowautomation or enterprise operations.

- Experience with LangGraph, AutoGen, CrewAI, SemanticKernel, AgentSea, OpenAI Agents SDK, MCP, vector databases, graph databases orsimilar 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 loggingand tool mediation.

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

- Experience designing or operating simulationenvironments, cyber ranges, replay systems, benchmark suites or adversarialtest harnesses.

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