Lead AI/ML Engineer

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 looking for a versatile Lead AI/ML Engineer to join the Our team, contributing to the design and deployment of scalable AI solutions across the full stack. This role blends machine learning engineering with frontend/backend development and cloud native microservices. You'll work closely with data scientists, MLOps engineers, and product teams to bring generative AI capabilities like RAG and LLM based systems into production.

Primary Responsibilities:

• Lead and conduct cutting-edge research in deep learning, foundation models, reinforcement learning, or multimodal AI to drive innovation in healthcare
• Lead the design, development, and deployment of AI and generative AI solutions to solve complex challenges in healthcare, especially within Safety, Regulatory, and Clinical operations
• Collaborate with cross-functional healthcare teams to integrate cloud computing and automation into digital health products and services
• Stay current with innovations in AI, cloud, and automation, applying them to enhance healthcare technology offerings
• Define and manage product roadmaps that align with patient needs, clinical workflows, and healthcare market trends
• Work closely with data scientists and engineers to gather and refine technical requirements for healthcare applications
• Improve scalability and performance of healthcare solutions using cloud infrastructure and automation tools
• Analyze clinical performance data and user feedback to drive continuous product improvement
• Mentor junior engineers, fostering a culture of innovation, learning, and technical excellence in healthcare tech
• Communicate product vision and progress to stakeholders, ensuring alignment with healthcare business goals
• Integrate text, image, and audio data using multimodal transformers to create comprehensive models for healthcare applications
• Develop graph-based RAG systems for contextual reasoning and incorporate knowledge connections from graph neural networks
• Design and implement real-time RAG systems to handle dynamic and up-to-date healthcare information
• Develop and fine-tune large language models (LLMs), diffusion models, and multi-agent systems
• 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

• Bachelor's degree in Computer Science, Engineering equivalent experience in AI/ML, or a practical experience Proven experience in leading AI/ML research projects and teams
• 12+ years of experience in AIML engineering and research
• Proven experience in engineering roles with a focus on AI, generative AI, cloud computing, and automation
• Experience with Python libraries for data science and machine learning (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch)
• Experience deploying ML models using Docker and Kubernetes
• Practical experience with cloud platforms like AWS, Azure, or Google Cloud
• Experience with graph-based RAGs for contextual reasoning and incorporating knowledge connections from graph neural networks
• Knowledge of model monitoring, logging, and performance tracking in production environments
• Familiarity with ML model lifecycle management using MLflow or Kubeflow
• Familiarity with multimodal transformers for integrating text, image, and audio data
• Familiarity with AIML governance, ethics, and responsible AI practices
• Proficient in cloud-based MLOps tools (e.g., AWS SageMaker, Azure ML, Google Vertex AI)
• Proficient in core data structures including Stacks, Linked Lists, Trees, and Graphs
• Proven skilled in setting up CI/CD pipelines for ML workflows using Jenkins, GitHub Actions, or Azure DevOps
• Proven solid programming skills in Python, TensorFlow, PyTorch, or similar frameworks
• Proven excellent problem-solving skills and the ability to work independently and collaboratively
• Proven solid communication skills and the ability to present complex technical concepts to non-technical stakeholders