Staff Engineer, AI Operations & Governance, Workplace AI (R5428)

This is a deeply technical individual contributor role reporting to the Head of AI Operations & Governance (Enterprise AI). This person will own significant portions of the technical operating load for workplace AI: platform configuration, connector and integration management, observability wiring, secrets and access hygiene, model/prompt lifecycle mechanics, and hands-on changes in production.

At the same time, the role will help translate production realities into governance artifacts, risk assessments, status updates, and training/enablement support — giving the Head a force multiplier who can operate at both the technical and “softer” layers of AI operations and governance.

This role will also serve as a hands-on technical responder for workplace AI incidents and production issues. The Staff Engineer is expected to investigate failures, troubleshoot across platforms, integrations, prompts, configurations, and access paths, implement mitigations or fixes where appropriate, and help restore service quickly while documenting root cause, lessons learned, and prevention steps.

· Implement and maintain AI platform configurations
Own day-to-day configuration of AI platforms and orchestration tools (models, routes, guardrails, tenants, policies, role mappings, prompt libraries, etc.), under the direction of the Head.
· Manage connectors, integrations, and data access paths
Design, configure, and maintain connectors and extensions into SaaS systems, data sources, and workflow tools; ensure connectivity is reliable, secure, and aligned with access policies.
· Own observability wiring for AI tools
Set up and maintain logging, metrics, and alerts for AI workflows and tools; make sure key signals (latency, errors, usage, drift indicators) are captured and visible to the team.
· Handle secrets and access hygiene
Implement secure storage and rotation for API keys, tokens, and credentials; maintain access control configurations and partner with Security/IT on reviews and remediation.
· Execute model and configuration changes in production
Implement model swaps, policy updates, prompt changes, version upgrades, and rollout plans based on decisions made by the Head; maintain detailed change records and rollback paths.
· Support technical evaluations and benchmarking
Run experiments and benchmarks on models, tools, and configurations; collect and summarize technical performance data to inform governance and roadmap decisions.
· Translate technical signals into governance and risk views
Help the Head interpret logs, metrics, and incidents into clear risk, reliability, and compliance narratives that can be shared with Security, Legal, HR, and business sponsors.
· Contribute to playbooks and training
Co-author technical sections of operational playbooks, runbooks, and training materials; occasionally participate in training or office hours to help users understand capabilities and guardrails.
· Coordinate and communicate on incidents and changes
Act as a technical point of contact in incidents: triage, investigate, propose mitigations, execute fixes, and document learnings; communicate clearly with non-technical stakeholders when needed.

· 4–7+ years in roles such as platform engineer, DevOps/SRE, ML/AI operations, or technical SaaS operations, with hands-on responsibility for production systems.
· Strong fluency in APIs, integrations, and infrastructure-as-config concepts; able to work in code/JSON/YAML configuration environments and with automation where appropriate.
· Hands-on experience with monitoring and observability tools (logs, metrics, alerts) and using them to diagnose issues and guide improvements.
· Practical experience working with at least one class of AI or automation platforms (LLM providers, AI productivity tools, RPA/workflow engines, or similar).
· Comfort with secure secrets management and access control practices (roles, permissions, key rotation, least-privilege patterns).
· Ability to document technical work and decisions clearly for both technical and non-technical audiences.
· Strong ownership mindset, bias to action, and comfort operating close to production in a high-stakes environment.

· Experience in ML/AI ops specifically (model deployment, evaluation, drift monitoring), even if not as a dedicated ML engineer.
· Familiarity with prompt engineering, policies/guardrails, and configuration patterns for LLM-based systems.
· Exposure to governance, compliance, or risk frameworks for data-driven or AI systems.
· Experience collaborating with Security, Legal, and business stakeholders on technical risk and mitigation.
· Prior involvement in incident response, change management, or on-call rotations for critical systems.