About the role
- Build and integrate agentic AI solutions and automations within the enterprise GRC platform to standardize workflows, automate evidence collection, and improve reporting.
- Develop control automations, evidence collectors, and governance tooling, reducing dependence on the broader engineering backlog.
- Engineer AI-assisted workflows to populate, maintain, and reconcile the enterprise Risk Register across technology domains.
- Automate linkage between risk entries, controls, and remediation tracking.
- Build AI-assisted tooling for policy drafting, framework crosswalks, annual review cadence, and exception workflows.
- Design agentic workflows using RAG, function/tool calling, and Model Context Protocol (MCP), with appropriate guardrails for accuracy, confidentiality, and IP boundaries.
- Implement human-in-the-loop checkpoints and monitoring so GRC leaders can supervise and validate agent outputs.
- Apply AI risk controls aligned to OWASP Top 10 for LLM Applications, NIST AI RMF, ISO/IEC 42001, and MITRE ATLAS.
- Build evaluation harnesses and observability for deployed agents - measuring grounding, accuracy, and consistency while minimizing hallucinations across GRC use cases.
- Implement guardrails, deployment gates, and immutable audit trails/logging so non-compliant or low-confidence outputs are caught before use.
- Maintain model documentation (model cards, data provenance) to support AI governance and regulatory defensibility.
- Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field; equivalent practical experience considered.
- Minimum 4+ years in software/AI engineering, with hands-on experience building LLM-powered applications and agentic workflows.
- Proficiency in Python (or comparable) and modern AI development tooling (e.g., Claude Code, Cursor, GitHub Copilot).
- Hands-on experience with LLM application patterns - prompt engineering, RAG, function/tool calling, agentic orchestration, and MCP.
- Familiarity with leading LLMs (Anthropic Claude, OpenAI GPT/o-series, Google Gemini, Meta Llama, Mistral) and model selection trade-offs (reasoning depth, context window, cost, latency, data residency).
- Working knowledge of the AI/LLM risk landscape: OWASP Top 10 for LLM Applications, NIST AI RMF, ISO/IEC 42001, MITRE ATLAS, and emerging regulation (EU AI Act, NYDFS AI guidance).
- Experience integrating with enterprise platforms and APIs; familiarity with GRC tooling (e.g., ServiceNow IRM, Archer, AuditBoard) a plus.
- Ability to translate GRC domain requirements into well-governed, production-grade automations with human-in-the-loop controls.
- Strong collaboration skills and the ability to partner with non-technical GRC stakeholders.
- Experience building agent evaluation frameworks, guardrails, prompt/version management, and observability/logging for production LLM systems.
- Familiarity with cloud ML platforms (AWS Bedrock/SageMaker, Azure AI, GCP Vertex) and CI/CD-integrated deployment gates.
- AI governance certification a plus (e.g., IAPP AIGP), including agentic architecture concepts.
- Preferred experience in the Property Management, Multifamily Housing, SaaS, FinTech, or PropTech industries.