Senior Software Engineer, AI Governance

Role overview

A senior software engineer role focused on turning AI governance from a committee function into real platform capability. The work centers on designing, building, and operating the technical controls, runtime enforcement, and monitoring layers that govern production AI systems used in clinical, diagnostic, and patient-facing workflows. Roughly three quarters of the time is hands-on engineering, with the remainder spent on cross-functional direction.

Responsibilities

- Build and own guardrail components inside an LLM gateway, including content filtering, output validation, PHI/PII detection, prompt injection defenses, session retention, and audit logging. - Engineer the governance layer for agentic runtimes and RAG infrastructure, covering policy hooks, risk-tier routing, retrieval filtering, citation integrity, and PHI exposure prevention. - Instrument every control with observability from day one so filtering, flagging, escalation, and drift are visible in production. - Design and implement an automated AI risk intake pipeline with structured intake, scoring, tiering, and routing aligned to NIST AI RMF, CHAI, and the EU AI Act. - Build and maintain production monitoring for accuracy, drift, misuse, and incident response tooling with detection, alert routing, and remediation tracking. - Lead technical due diligence for third-party AI vendors and foundation model providers, covering data residency, model transparency, API security, and contractual controls.

Requirements

- 7+ years of software engineering experience, including at least 3 years directly on AI/ML systems, LLM infrastructure, or AI safety and governance engineering. - Hands-on experience shipping production AI systems such as LLM pipelines, RAG architectures, agentic runtimes, or AI observability tooling. - Practical knowledge of production LLM risks (hallucination, bias, prompt injection, data leakage, model drift) and the technical mitigations for them. - Strong Python proficiency with experience building production API integrations, middleware, and data pipelines. - Background in ML observability and monitoring, including drift thresholds and production instrumentation. - Experience in a regulated environment (healthcare, life sciences, or financial services) with real HIPAA or equivalent compliance obligations and PHI/PII handling. - Working knowledge of NIST AI RMF or ISO 42001 applied through actual controls rather than documentation alone.

Benefits and work setup

- Comprehensive medical, dental, vision, life, and disability coverage for eligible employees and dependents. - Free testing plus fertility care benefits for employees and their immediate families. - Pregnancy and baby bonding leave, 401k, commuter benefits, and a referral program.