Join Optum, a global leader in health optimization, and drive innovation at the intersection of AI, machine learning, and healthcare. We are seeking a visionary AI Engineering Lead to architect and deliver enterprise-grade AI solutions, mentor emerging talent, and spearhead advancements in Generative AI. This role offers a unique opportunity to shape our AI engineering culture, technology strategy, and customer-centric solutions, with a strong emphasis on accuracy, scalability, and robust design.

Our mission is to help millions live healthier lives by connecting them with the care, pharmacy benefits, data, and resources they need. You will contribute to a culture of inclusion and collaboration, working alongside talented peers with comprehensive benefits and career development opportunities. Make a significant impact on communities worldwide as we advance health optimization globally. Start your journey with us by Caring, Connecting, and Growing together.

Lead the design, development, and implementation of scalable AI/ML solutions using Python, SQL, pandas, numpy, and modern ML frameworks like TensorFlow, PyTorch, and Scikit Learn.

Spearhead the predictive maintenance practice for critical medical imaging equipment such as MRI and CT scanners.

Oversee end-to-end ML workflows, from data preparation and feature engineering to model evaluation, deployment, and continuous monitoring. Guide model selection, optimization, and tuning, ensuring solutions balance performance, explainability, cost, and Responsible AI principles.

Build and maintain production-grade ML pipelines using orchestration and experiment tracking tools like MLflow, Feature Stores, ONNX, and vector databases. Lead drift analysis, observability, and model health monitoring for ongoing accuracy and reliability.

Design and support scalable, multi-cloud ML infrastructure (Azure/GCP primary, AWS optional), incorporating best practices for distributed data processing. Collaborate with data engineering teams on real-time and batch pipelines.

Work closely with engineering, product, data science, and UX teams to translate business requirements into actionable technical solutions. Review and guide the creation of architecture diagrams and technical specifications.

Mentor and coach data scientists and ML engineers on best practices in model development, coding standards, optimization, and cloud-native ML deployment. Ensure ML systems meet high standards of scalability, security, reliability, automation, and observability.

Collaborate with front-end teams for seamless integration of ML components into product workflows. Stay current with emerging AI/ML trends and research, evaluating their applicability to current and future projects.

Communicate technical concepts and recommendations clearly to both technical and non-technical stakeholders. Document methodologies, experiments, and findings for knowledge sharing and maintainability.

Possess a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.

Bring 5+ years of experience in Data Science, AI/ML, with at least 2 years in leadership roles.

Demonstrate hands-on experience delivering production-grade AI/ML projects across the full lifecycle, including EDA, model development, tuning, and drift analysis.

Experience in Predictive Maintenance using AI, IoT, Smart Manufacturing, and Prognostics and Health Management (PHM) is essential.

Proficiency with Time Series models, foundational models, signal processing, anomaly detection, and Remaining Useful Life (RUL) estimation is required.

Solid programming skills in Python, coupled with experience in pandas, numpy, and SQL, are necessary. A strong understanding of mathematical and statistical concepts is crucial.

Expertise in frameworks such as Scikit Learn, TensorFlow, and PyTorch is expected.

Exhibit excellent problem-solving and analytical thinking skills.

Possess strong communication and collaboration skills to thrive in a team environment.

Familiarity with Big Data technologies (PySpark, Hadoop) and data streaming tools (Kafka) is preferred.