Key skills
Machine Learning EngineeringMLOpsAI Platform EngineeringPythonPyTorchTensorFlowscikit-learnML Lifecycle PlatformsMLflowKubeflowSageMakerAzure MLGCP Vertex AICloud-native ML PlatformsDockerKubernetesDistributed SystemsDistributed Data ProcessingSparkRayAirflowDagsterPrefectGenerative AILarge Language Models (LLMs)Prompt EngineeringPrompt ChainingFine-tuning Instruction TuningFine-tuning LoRA/qLoRAAIMachine LearningMLGenAILLMClaudeLangChainLlamaIndexAWSAzureGCPVertex AICachingPerformance Optimization
About this role
Optum is a global leader in health care innovation, and they are seeking an AI/ML Engineer to join their team. The role involves building and operating scalable machine learning platforms and production ML systems, driving the design and implementation of ML infrastructure, and collaborating closely with data science and engineering teams.
Responsibilities:
- Build enterprise ML and GenAI platforms supporting experimentation, model training, evaluation, deployment, monitoring, and lifecycle management
- Productionize machine learning and generative AI models using batch and real-time inference architectures
- Build and operate MLOps and LLMOps pipelines including CI/CT/CD workflows for model testing, validation, versioning, and promotion across environments
- Develop scalable, cloud-native ML infrastructure using Docker, Kubernetes, and cloud ML platforms such as AWS SageMaker, Azure ML, or GCP Vertex AI
- Build and manage LLM application stacks, including LLM gateways, orchestration layers, model routing, caching, and cost/performance optimization
- Implement model monitoring and lifecycle management systems to track drift, latency, bias, and data quality while enabling automated retraining
- Ensure governance, security, and compliance of ML systems including lineage, auditability, reproducibility, and observability
- Partner with data scientists, data engineers, and software engineers to define production ML standards and scalable AI solutions
Requirements:
- 5+ years of experience in machine learning engineering, MLOps, or AI platform engineering building production ML systems and scalable model pipelines
- 4+ years of experience programming in Python for ML systems with familiarity with frameworks such as PyTorch, TensorFlow, or scikit-learn
- 3+ years of experience working with ML lifecycle platforms such as MLflow, Kubeflow, SageMaker, Azure ML, or GCP Vertex AI
- 3+ years of experience building cloud-native ML platforms using Docker, Kubernetes, and distributed systems
- 3+ years of experience working with distributed data processing and orchestration tools such as Spark, Ray, Airflow, Dagster, or Prefect
- 3+ years of experience with Generative AI and LLMs, including prompt engineering, prompt chaining, and fine-tuning (instruction tuning and LoRA/qLoRA)
- Master's degree in Computer Science, Engineering, Data Science, or related discipline
- Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel)
- Experience working in regulated or enterprise environments, with emphasis on security, compliance, and responsible AI
- Experience with vibe coding tools, such as Cursor, Claude Code, and Replit
- Experience operating multi-cloud or hybrid ML platforms
- Experience in Healthcare or Life Sciences
- Knowledge of LLM cost optimization and performance tuning techniques
- Exposure to knowledge graphs or hybrid search
- Contributions to open-source ML or MLOps tooling
- All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy