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.