AI / ML Engineer
unitedhealthgroup
Job Description
- Lead the design, development, and implementation of scalable AI/ML solutions, leveraging Python, SQL, pandas, numpy, and modern ML frameworks (TensorFlow, PyTorch, Scikit Learn)
- Oversee end to end ML workflows-from data preparation, feature engineering, EDA, and model development to 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 with orchestration and experiment tracking tools such as MLflow, Feature Stores, ONNX, and vector databases
- Lead drift analysis, observability, and model health monitoring to ensure ongoing accuracy, reliability, and robustness in production environments
- Design and support scalable, multi cloud ML infrastructure (Azure/GCP primary, AWS optional), incorporating 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/Big Query, Dataflow/Databricks, and Kafka/EventHub/Pub Sub
- Work closely with engineering, product, data science, and UX 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 of scalability, security, reliability, automation, observability, and operational excellence
- Collaborate with front end teams where needed to ensure ML components integrate seamlessly with product workflows and user-facing experiences
- Stay current with emerging AI/ML tools, frameworks, and research; evaluate applicability to current and future projects
- Communicate technical concepts, design trade-offs, and recommendations clearly to both technical and non technical stakeholders, including leadership
- Document methodologies, experiments, findings, and implementation details to ensure maintainability and knowledge sharing across teams
- Comply with company policies, employment terms, and organizational directives related to work arrangements and project assignments
- 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