Lead AI or ML Engineer - Python, Typescript, React, Agentic Model

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We are seeking a Lead AI/ML Engineer to drive the design, development, and operationalization of AI/ML solutions that improve the reliability, efficiency, and clinical impact of workflows and integrations. This is a hands-on technical leadership role responsible for delivering production-grade ML systems end-to-end, data, modeling, MLOps, monitoring, and continuous improvement, while mentoring engineers and partnering closely with product, clinical, platform, and operations teams.

You Will Help Build AI Capabilities That Support

• Clinical workflow intelligence (prioritization, recommendations, decision support)
• Operational reliability (anomaly detection, incident prediction, noise reduction)
• Automation and agentic workflows (triage, routing, diagnostics, self-heal where appropriate)
• Improved data quality, latency visibility, and integration observability

Primary Responsibilities

• Technical Leadership & Delivery (Hands-On)

• Lead the architecture and implementation of scalable AI/ML solutions integrated into the ecosystem (APIs, event streams, workflow engines, and integration layers)
• Own end-to-end ML lifecycle: problem framing, feature engineering, model development, validation, deployment, monitoring, drift detection, retraining strategy
• Establish best practices for MLOps: CI/CD for ML, model registries, automated evaluation gates, reproducible training, and secure deployment patterns
• Build production-grade inference services (real-time and batch) with clear SLOs, instrumentation, and rollback strategies

• Define and enforce data governance for ML features and training datasets (quality checks, lineage, documentation)

• Clinical Platform Impact

• Partner with product and clinical stakeholders to identify high-impact use cases and translate them into measurable outcomes (quality, productivity, stability, member/patient impact)

• Embed AI into workflows responsibly, with explainability, auditing, and human-in-the-loop guardrails

• Reliability, Observability & Responsible AI

• Implement ML monitoring (performance, drift, bias checks where applicable) and integrate signals into operational dashboards and alerting
• Ensure solutions meet security and compliance needs (PHI/PII protection, least-privilege access, auditability)

• Drive responsible AI practices: evaluation transparency, documentation, risk assessment, and safe deployment patterns

• People Leadership & Collaboration

• Mentor and guide ML engineers and software engineers, raising the bar on engineering quality, design rigor, and operational excellence
• Lead technical design reviews, influence platform direction, and align teams across engineering, data, operations, and product

• Act as a team player: unblock others, foster shared ownership, and improve execution predictability.

• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications

• Undergraduate degree experience
• 10+ years of software engineering experience with 3+ years building and deploying ML systems into production
• Proven hands-on experience delivering end-to-end ML solutions (data → model → deployment → monitoring → iteration)
• Experience building API-based inference services and data pipelines in cloud-native environments (containerization, orchestration, CI/CD)
• Experience collaborating across function…