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.

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