Manager, AI Operations
New
X
XsolisHealthcare Technology
RemoteFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- 7+ years of experience in AI/ML operations, MLOps or production data science, including 2+ years of people or team leadership.
- Required Skills
- AWSMachine LearningDatadogMLOpsGenerative AI
Requirements
- 7+ years of experience in AI/ML operations, MLOps or production data science.
- 2+ years of people or team leadership experience.
- Hands-on experience operating traditional ML and GenAI systems in production (deployment, monitoring, incident response).
- Strong understanding of observability practices like drift detection, model performance monitoring, logging, and alerting.
- Experience partnering with Security and Infrastructure teams on production risk and access controls.
- Excellent communication skills with the ability to translate operational risk for stakeholders.
- Prior experience in healthcare, health tech, payer, or provider organizations (preferred).
- Experience operating agentic AI systems (preferred).
- Familiarity with MLOps/observability tooling like Datadog, AWS Cloud Watch, LangFuse, and AWS platform capabilities (preferred).
- Experience working alongside a dedicated AI Governance function (preferred).
Responsibilities
- Own deployment support, monitoring, and day-to-day reliability of AI systems in production across traditional ML, GenAI, and agentic AI.
- Lead incident response and root-cause troubleshooting for AI/ML services, including agentic systems with autonomous or tool-using behavior.
- Partner with MLOps, Cloud Engineering, and Infrastructure teams to build and maintain robust deployment pipelines.
- Build and maintain observability practices across all AI system types, including drift detection, latency, uptime, and agent action-level tracing.
- Define and track production health metrics, establish dashboards, and route performance degradation to build teams.
- Serve as the primary liaison between AI systems and enterprise Security, Infrastructure, and IT teams.
- Support AI Governance by instrumenting and operating fairness, safety, and bias-monitoring metrics.
- Lead and grow a production support and observability team, setting priorities and mentoring team members.
- Establish Ethical AI best practices and SOPs across the AI stack while managing costs.
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