Staff Engineer, AI Operations & Governance, Workplace AI

Accountabilities:: Manage day-to-day configurations for AI platforms and orchestration tools, including models, routing, guardrails, tenants, policies, role mappings, and prompt libraries. Design, configure, and maintain connectors and integrations with SaaS applications, data sources, and workflow tools while ensuring secure and reliable data access. Build and maintain observability across AI workflows, including logging, metrics, alerts, latency, errors, usage patterns, and potential drift indicators. Implement secure secrets management and access controls, including API key and credential rotation, permissions, and least-privilege practices in collaboration with Security and IT. Execute production changes such as model replacements, policy updates, prompt modifications, version upgrades, and controlled rollouts while maintaining change records and rollback procedures. Conduct technical evaluations and benchmarking of AI models, tools, and configurations, summarizing results to support governance and roadmap decisions. Translate technical logs, metrics, incidents, and operational findings into clear risk, reliability, compliance, and governance perspectives for technical and business stakeholders. Contribute to operational playbooks, runbooks, documentation, and training materials, and occasionally support user training or technical office hours. Serve as a technical incident responder by triaging failures, investigating root causes, proposing and implementing mitigations, documenting lessons learned, and communicating clearly with non-technical stakeholders. Requirements 4–7+ years of experience in platform engineering, DevOps/SRE, ML/AI operations, technical SaaS operations, or a similar discipline involving hands-on production systems. Strong knowledge of APIs, integrations, infrastructure-as-code concepts, and configuration environments using code, JSON, YAML, and automation. Hands-on experience with monitoring and observability tools, including logs, metrics, and alerting systems, with the ability to use operational data to diagnose and improve systems. Practical experience with at least one AI or automation platform, such as LLM providers, AI productivity platforms, RPA solutions, workflow engines, or similar technologies. Experience with secure secrets management, access controls, role-based permissions, key rotation, and least-privilege security practices. Strong technical documentation skills, with the ability to communicate engineering decisions and operational information to both technical and non-technical audiences. Strong ownership, initiative, problem-solving ability, and comfort working directly with production systems in a high-stakes environment. Experience in ML/AI operations, including model deployment, evaluation, or drift monitoring, is preferred. Familiarity with prompt engineering, LLM policies, guardrails, and configuration patterns is an advantage. Exposure to AI governance, compliance, or risk frameworks for data-driven and AI systems is beneficial. Experience collaborating with Security, Legal, and business stakeholders on technical risks and mitigation strategies is preferred. Prior experience with incident response, change management, or on-call rotations for critical systems is a plus. Benefits Annual salary range of $200,000–$300,000, with compensation influenced by experience, skills, certifications, and work location. Full-time regular employees receive salary within the stated range plus bonus, benefits, and equity. Temporary employees receive compensation within the stated range plus an applicable temporary benefits package after 60 days of employment. Comprehensive employee benefits are available for eligible full-time employees. Equity participation is included in the regular employee offer package. Offers are contingent on successful background screening and, where applicable, reference checks. Remote work arrangement within the United States. Opportunity to work on workplace AI operations, governance, reliability, security, and emerging AI technologies in a technically demanding environment.