Lead AI/ML Engineer - Clinical Platform - Remote

UnitedHealth Group•Hartford, CT

2d•Remote

About The Position

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 diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity 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 Optum Clinical Manager (OCM) 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 You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

Requirements

Bachelor's Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, or a related STEM field.
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 functions (product, operations, data, security, compliance) and translating needs into technical solutions
Solid skills in Python and modern ML libraries (e.g., PyTorch, TensorFlow, scikit-learn), plus strong software engineering fundamentals
Expertise in MLOps practices: model versioning, reproducibility, automated testing/validation, monitoring, drift detection
Solid understanding of data engineering concepts (feature stores, streaming/batch processing, data quality checks, lineage)
Proven solid leadership behaviors: mentoring, influencing without authority, driving clarity, and executing with accountability
Proven excellent communication skills—can explain complex ML concepts to non-ML stakeholders and align on measurable outcomes

Nice To Haves

Master's Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, or a related STEM field.
Experience with healthcare systems and workflows (care management, utilization management, clinical operations) and/or working with PHI/PII in regulated environments (HIPAA-aligned controls)
Familiarity with clinical data standards and patterns (claims/encounters, care plans, HL7/FHIR concepts—where relevant)
Experience with LLMs / GenAI for enterprise use cases (summarization, classification, retrieval, workflow copilots), including: RAG architectures, evaluation frameworks, prompt/version control, safety guardrails
Applied experience in one or more areas: Anomaly detection and time-series modeling, Ranking/recommendation systems, NLP for clinical/operational text, Causal inference / uplift modeling for operational optimization
Experience with observability platforms and building ML-driven alerting/noise reduction (AIOps)
Experience designing event-driven architectures (e.g., Kafka-style streaming), feature computation at scale, and real-time decisioning
Experience with security-by-design and governance (model documentation, audit trails, approvals)
Experience leading technical roadmaps, shaping platform standards, and coordinating across multiple teams
Track record of establishing ML engineering standards (coding practices, model review process, reusable components)

Responsibilities

Lead the architecture and implementation of scalable AI/ML solutions integrated into the OCM 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)
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
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
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