About the role

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.

The AI/ML Engineer (SG27) will design, develop, and operationalize AI/ML, Generative AI, LLM, and Agentic AI solutions that drive intelligent automation and business value. The role requires strong expertise in PySpark, Scala Spark, Azure Cloud, and modern AI/ML technologies to build scalable batch, streaming, and cloud-native data platforms.

The engineer will collaborate with data science, architecture, and business teams to deliver enterprise AI solutions, implement MLOps best practices, and ensure the reliability, scalability, and governance of AI applications. Additionally, they will contribute to AI strategy, data platform architecture, automation frameworks, and continuous innovation across the enterprise data ecosystem.

Primary Responsibilities:

Partner with Data Science and AI/ML teams to design, develop, and deploy machine learning, Generative AI, and Agentic AI solutions that drive predictive analytics, automation, and business value
Build and operationalize LLM-powered applications, including RAG frameworks, AI agents, prompt engineering, orchestration workflows, and contextual data enrichment
Deploy, monitor, and govern AI/ML models using Azure ML, Databricks ML, Azure AI Search, and modern MLOps practices
Integrate AI/ML and LLM capabilities into enterprise data platforms, ensuring scalability, security, compliance, and responsible AI practices
Design and develop scalable AI-enabled data pipelines using PySpark, Scala Spark, and Azure Cloud technologies
Collaborate with architecture, data engineering, and business teams to translate business requirements into production-ready AI solutions
Develop and optimize RAG, vector search, semantic retrieval, and knowledge management frameworks for enterprise AI applications
Build and maintain batch, streaming, and real-time AI data processing pipelines leveraging Azure Event Hub and Spark Structured Streaming
Implement CI/CD and MLOps automation using GitHub Actions, Jenkins, Docker, Kubernetes, and Azure DevOps
Evaluate emerging AI technologies, frameworks, and tools to drive innovation and continuous improvement
Develop prototypes, proof-of-concepts (POCs), and production-grade AI applications while participating in architecture, design, and code reviews
Create technical documentation and provide support for AI/ML application deployments, monitoring, and incident resolution
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

Required Qualifications:

Bachelor’s or Master’s degree in Computer Science, Information Technology, or equivalent
5+ years of professional experience in AI/ML engineering, including machine learning, Generative AI, LLM-based applications, Agentic AI frameworks, and Big data platform development
Solid hands-on experience with Python and Scala
Experience building large-scale batch and streaming data processing systems
Proven experience working in cloud environments, preferably Microsoft Azure
Hands-on experience with Snowflake and strong expertise in SQL and PL/SQL
Experience with Shell scripting for automation and operational support
Experience developing, training, fine-tuning, and deploying AI/ML models using frameworks such as scikit-learn, TensorFlow, PyTorch, and Generative AI/LLM ecosystems
Experience building and managing CI/CD pipelines using Jenkins, GitHub Actions, and Git-based workflows
Hands-on experience with Docker, Kubernetes, and modern DevOps practices
Experience working in Agile development environments
Understanding of the ML model lifecycle, including training, evaluation, deployment, and monitoring
Good understanding of US healthcare domain is an added advantage
Proven hands-on exposure to LLMs (e.g., OpenAI GPT, Azure OpenAI), including prompt design, fine-tuning concepts, and secure workflow integration
Proven expertise in Apache Spark and solid understanding of Hadoop ecosystem concepts
Proven solid collaboration skills with cross-functional teams and stakeholders
Proven ability to analyze complex problems and deliver solution-focused outcomes

Preferred Qualification:

Familiarity with Microsoft Copilot Studio and Azure AI Search for building and deploying conversational AI solutions