Lead AI or ML Engineer

Role Overview: As a Lead AI/ML Engineer at Optum, you will drive the design, development, and operationalization of AI/ML solutions that enhance the reliability, efficiency, and clinical impact of workflows and integrations. Your role will involve hands-on technical leadership in delivering end-to-end production-grade ML systems, encompassing data, modeling, MLOps, monitoring, and continuous improvement. Additionally, you will mentor engineers and collaborate closely with product, clinical, platform, and operations teams to achieve impactful outcomes. Key Responsibilities: - Lead the architecture and implementation of scalable AI/ML solutions integrated into the ecosystem, including APIs, event streams, workflow engines, and integration layers - Take ownership of the end-to-end ML lifecycle, from problem framing and feature engineering to model development, deployment, monitoring, drift detection, and retraining strategy - Establish best practices for MLOps, such as 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 defined SLOs, instrumentation, and rollback strategies - Define and enforce data governance for ML features and training datasets, including quality checks, lineage, and documentation - Partner with product and clinical stakeholders to identify high-impact use cases and translate them into measurable outcomes - Embed AI into workflows responsibly, ensuring explainability, auditing, and human-in-the-loop guardrails - Implement ML monitoring for performance, drift, bias checks, and integrate signals into operational dashboards and alerting - Ensure solutions meet security and compliance requirements, including PHI/PII protection, least-privilege access, and auditability - Drive responsible AI practices by focusing on evaluation transparency, documentation, risk assessment, and safe deployment patterns - Mentor and guide ML engineers and software engineers to elevate engineering quality, design rigor, and operational excellence - Lead technical design reviews, influence platform direction, and align teams across engineering, data, operations, and product Qualifications Required: - Undergraduate degree and 10+ years of software engineering experience, with at least 3 years dedicated to building and deploying ML systems into production - Proven hands-on experience in delivering end-to-end ML solutions, encompassing data, model, deployment, monitoring, and iteration - Experience building API-based inference services and data pipelines in cloud-native environments, including containerization, orchestration, and CI/CD - Collaboration across functions, translating needs into technical solutions - Solid understanding of data engineering concepts, Python skills, and modern ML libraries such as PyTorch, TensorFlow, and scikit-learn - Expertise in MLOps practices, including model versioning, reproducibility, automated testing/validation, monitoring, and drift detection - Strong leadership behaviors, excellent communication skills, and the ability to explain complex ML concepts to non-ML stakeholders (Note: Additional details about the company have been omitted as they were not explicitly mentioned in the provided job description.) Role Overview: As a Lead AI/ML Engineer at Optum, you will drive the design, development, and operationalization of AI/ML solutions that enhance the reliability, efficiency, and clinical impact of workflows and integrations. Your role will involve hands-on technical leadership in delivering end-to-end production-grade ML systems, encompassing data, modeling, MLOps, monitoring, and continuous improvement. Additionally, you will mentor engineers and collaborate closely with product, clinical, platform, and operations teams to achieve impactful outcomes. Key Responsibilities: - Lead the architecture and implementation of scalable AI/ML solutions integrated into the ecosystem, including APIs, event streams, workflow engines, and integration layers - Take ownership of the end-to-end ML lifecycle, from problem framing and feature engineering to model development, deployment, monitoring, drift detection, and retraining strategy - Establish best practices for MLOps, such as 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 defined SLOs, instrumentation, and rollback strategies - Define and enforce data governance for ML features and training datasets, including quality checks, lineage, and documentation - Partner with product and clinical stakeholders to identify high-impact use cases and translate them into measurable outcomes - Embed AI into workflows responsibly, ensuring explainability, auditing, and human-in-the-loop guardrails - Implement ML monitoring for performance, drift, bias checks, and integrate signals into