Associate 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.

Primary Responsibilities

• Assist in the design, development, testing, and deployment of AI/ML solutions using Python and modern machine learning frameworks
• Support data preparation, exploratory data analysis (EDA), feature engineering, model training, and validation activities
• Develop and maintain machine learning models using tools such as Scikit-Learn, TensorFlow, or PyTorch
• Collaborate with data engineers and data scientists to build scalable data pipelines and AI-enabled applications
• Participate in model performance monitoring, drift analysis, and continuous improvement of deployed models
• Support implementation of ML workflows, experiment tracking, and model documentation to ensure reproducibility and maintainability
• Work with cross-functional teams to understand business requirements and translate them into AI/ML solutions
• Contribute to the adoption of Responsible AI practices, ensuring fairness, reliability, explainability, and data privacy
• Create and maintain technical documentation, project artifacts, and knowledge-sharing materials
• 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
  
  

• Graduate degree or equivalent experience
• Proven ability to lead the design, development, and implementation of scalable AI/ML solutions, leveraging Python, SQL, pandas, numpy, and modern ML frameworks (TensorFlow, PyTorch, Scikit Learn)
• Proven ability to oversee end to end ML workflows-from data preparation, feature engineering, EDA, and model development to evaluation, deployment, and continuous monitoring
• Proven ability to guide model selection, optimization, and tuning, ensuring solutions balance performance, explainability, cost, and Responsible AI principles
• Proven ability to build and maintain production grade ML pipelines with orchestration and experiment tracking tools such as MLflow, Feature Stores, ONNX, and vector databases
• Proven ability to lead drift analysis, observability, and model health monitoring to ensure ongoing accuracy, reliability, and robustness in production environments
• Proven ability to design and support scalable, multi cloud ML infrastructure (Azure/GCP primary, AWS optional), incorporating best practices for compute, storage, networking, and distributed data processing
• Proven ability to collaborate with data engineering teams to integrate real time and batch pipelines built on Synapse/BigQuery, Dataflow/Databricks, and Kafka/EventHub/PubSub
• Proven ability to work closely with engineering, product, data science, and UX to translate business requirements into actionable technical solutions and architecture patterns
• Proven ability to review and guide the creation of architecture diagrams, C4 models, ADRs, and technical specifications as part of engineering governance
• Proven ability to mentor and coach data scientists and ML engineers on best practices in model development, experimentation, coding standards, optimization, and cloud native ML deployment
• Proven ability to ensure ML systems meet high standards of scalability, security, reliability, automation, observability, and operational excellence
• Proven ability to collaborate with front end teams where needed to ensure ML components integrate seamlessly with product workflows and user-facing experiences
• Proven ability to stay current with emerging AI/ML tools, frameworks, and research; evaluate applicability to current and future projects