Join Optum, a global health technology leader, where your work directly enhances millions of lives. We foster an inclusive environment with talented colleagues, comprehensive benefits, and robust career development. Help us advance health optimization worldwide by Caring, Connecting, and Growing together. We are seeking a visionary AI Engineering Lead to architect and deploy enterprise-grade AI solutions, focusing on Generative AI innovation. This role is crucial for shaping our AI engineering culture, technology strategy, and customer-centric solutions with a strong emphasis on accuracy, scalability, performance, and robust design.

This leadership position requires a blend of advanced AI expertise, strategic foresight, and a passion for mentoring emerging talent. You will play a pivotal role in driving innovation and ensuring our AI initiatives are at the forefront of the industry, delivering measurable impact in healthcare.

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.

Oversee the predictive maintenance practice for critical medical imaging equipment, including MRI and CT Scanners.

Manage end-to-end ML workflows, encompassing data preparation, feature engineering, exploratory data analysis (EDA), model development, evaluation, deployment, and continuous monitoring.

Guide model selection, optimization, and tuning to ensure solutions balance performance, explainability, cost, and Responsible AI principles.

Build and maintain production-grade ML pipelines using orchestration and experiment tracking tools such as MLflow, Feature Stores, ONNX, and vector databases.

Lead drift analysis, observability, and model health monitoring to guarantee ongoing accuracy, reliability, and robustness in production.

Design and support scalable, multi-cloud ML infrastructure (Azure/GCP primary, AWS optional), implementing best practices for compute, storage, networking, and distributed data processing.

Collaborate with data engineering teams to integrate real-time and batch pipelines built on Synapse/BigQuery, Dataflow/Databricks, and Kafka/EventHub/PubSub.

Work closely with engineering, product, data science, and UX teams to translate business requirements into actionable technical solutions and architecture patterns.

Review and guide the creation of architecture diagrams, C4 models, ADRs, and technical specifications as part of engineering governance.

Mentor and coach data scientists and ML engineers on best practices in model development, experimentation, coding standards, optimization, and cloud-native ML deployment.

Ensure ML systems meet high standards for scalability, security, reliability, automation, observability, and operational excellence.

Collaborate with front-end teams to ensure seamless integration of ML components with product workflows and user-facing experiences.

Stay current with emerging AI/ML tools, frameworks, and research, evaluating their applicability to current and future projects.

Communicate technical concepts, design trade-offs, and recommendations clearly to both technical and non-technical stakeholders.

Document methodologies, experiments, findings, and implementation details for maintainability and knowledge sharing.

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

Possess over 5 years of experience in Data Science, AI/ML, or a similar role, including at least 2 years in leadership positions.

Demonstrate hands-on experience in delivering production-grade AI/ML projects and managing the full lifecycle of AI/ML projects from EDA to model deployment and drift analysis.

Exhibit expertise in Predictive Maintenance using AI, IoT, Sensors, Smart Manufacturing, Uptime of MRI and CT Scanners, PHM, and Digital Twins.

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

Experience with streaming data, IoT architectures and tools, edge devices, and systems integration.

Solid programming skills in Python, complemented by experience with pandas, numpy, and SQL.

Possess a strong understanding of mathematical and statistical concepts.

Proficiency in frameworks such as Scikit Learn, TensorFlow, and PyTorch.

Showcase proven excellent problem-solving and analytical thinking skills.

Demonstrate solid communication and collaboration skills for effective teamwork.

Knowledge of Big Data technologies (PySpark, Hadoop) and data streaming tools (Kafka, etc.) is preferred, along with familiarity with IoT and medical equipment predictive maintenance.