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. We are seeking a Assoicate AI/ML Engineer with strong software engineering fundamentals and growing depth in AI/ML and data platforms. This role emphasizes building production‑ready, scalable AI services, applying Generative AI techniques, and continuously expanding expertise across machine learning and data engineering domains. Primary Responsibilities: AI/ML Solution Development Design, implement, and optimize AI and machine learning solutions, including statistical models, deep learning, and Generative AI systems Model Training, Evaluation & Optimization Execute proof‑of‑concepts, train models at scale, and baseline performance using quantitative evaluation metrics Platform & Infrastructure Engineering Build and operate large‑scale training and inference pipelines using Databricks, PySpark, and cloud platforms (AWS, Azure, GCP) Generative AI & Advanced Techniques Apply RAG, LangChain, and Vector Databases to develop GenAI solutions Optimize and quantize models to improve performance, scalability, and cost efficiency Software Engineering & APIs Develop REST and FastAPI services, containerize solutions using Docker, and integrate UI tools such as Streamlit or Flask Collaboration & Communication Partner with cross‑functional teams to translate business needs into clear, scalable AI solutions, and present insights effectively Leadership, Mentorship & Culture Mentor engineers, participate in design and architecture reviews, and uphold standards for quality, safety, and trust 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, Data Science, or a related field Software & AI/ML Engineering Experience 1+ years of professional software engineering experience, delivering high‑quality, production‑grade commercial applications end to end 1+ years of AI/ML engineering experience, including deploying models at scale and contributing to technical leadership across AI initiatives Demonstrated ability to design, build, deploy, and operate production‑ready services, including CI/CD and cloud infrastructure Programming & Systems Expertise 1+ years of hands‑on experience with Java, Python, SQL, and scripting Proven solid foundation in clean, maintainable code, system design, API development, and modern software engineering best practices Cloud & Data Platform Experience 1+ years of experience across AWS, Azure, and GCP, with deeper hands‑on experience in AWS and cloud‑native architectures Knowledge with Databricks, MongoDB, PySpark/SparkSQL, and data pipeline implementation Familiarity with Hadoop ecosystems and distributed data processing MLOps, Infrastructure & Governance Experience with data governance concepts, including access control and platform‑level controls in Databricks (Delta Lake, Unity Catalog) Working knowledge of MLOps practices, including model lifecycle management and operationalization Familiarity with Infrastructure as Code using Terraform and CloudFormation Technical Expertise Solid knowledge of AI/ML frameworks, orchestration tools, and scalable architectures Proficiency with big data technologies, including Spark, Hadoop, and Kafka Solid foundation in data science principles, including statistics, probability theory, optimization, simulation, and data modeling Candidate Profile & Growth Mindset Solid alignment is with software engineering fundamentals, including system design, clean and maintainable coding, and building production‑ready services end to end, from development through deployment and cloud infrastructure Comfortable working across AWS and cloud‑native architectures, with increasing hands‑on application of AI concepts, particularly LLMs and RAG‑based solutions Completed AI Dojo Generative AI training, strengthening applied Generative AI foundations and practical implementation skills Actively