Primary Responsibilities:- Design, develop, and deliver production-grade AI/ML and Generative AI solutions within the OQGA platform, including classification, forecasting, NLP, deep learning, and LLM-powered applications to improve platform efficiency and automation
- Build and maintain scalable batch and API-based data pipelines, working with large healthcare and quality datasets; write high-quality, testable code in Java, Python (pandas, NumPy), and SQL aligned to enterprise engineering standards
- Implement LLM-based capabilities, including prompt engineering, fine-tuning, Retrieval Augmented Generation (RAG), vector databases/indexing, evaluation frameworks, and safety/guardrails to ensure secure and production-ready solutions
- Contribute to LLMOps and DevOps practices, including CI/CD pipelines, model and prompt versioning, experiment tracking, monitoring, drift detection, A/B testing, and cost/performance optimization
- Collaborate with architects and senior engineers to implement system designs and technical solutions, translating architectural guidance into scalable and maintainable code
- Analyze business and technical requirements and convert them into efficient, scalable system designs and implementations, considering trade-offs across performance, accuracy, cost, and compliance
- Partner with Product, QA, and Operations teams to refine requirements, estimate work, and deliver features aligned with Agile development practices
- Ensure operational excellence by supporting deployment, monitoring, and maintenance of OQGA services across DEV, TEST, UAT/UATP, STAGE, and PROD environments; actively participate in incident resolution and RCA
- Follow and contribute to engineering best practices for code quality, unit/integration testing, documentation, security, and responsible AI development, delivering reliable solutions within defined timelines
- Continuously enhance technical skills and contribute to team knowledge sharing, supporting code reviews and collaborating effectively within the OQGA engineering team
Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear directions on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:- Bachelor's degree with 5+ years OR Master's with 3+ years OR PhD with 2+ years of experience in applied data science or advanced software engineering roles, with hands-on involvement in building and supporting production systems
- 2+ years of experience in Python, with hands-on experience using modern AI/ML libraries and a demonstrated ability to write clean, maintainable, and production-quality code
- 2+ years of experience with Java and developing enterprise applications using frameworks such as Spring / Spring Boot, with a good understanding of performance, scalability, and reliability
- 2+ years of AI/ML programming experience, including hands-on work with LLM frameworks and vector databases (e.g., LangChain, LlamaIndex, LangGraph, or similar)
- 2+ years of experience designing, developing, and deploying machine learning and Generative AI solutions in production environments, preferably within regulated or enterprise domains
- 2+ years of experience and working knowledge of LLMs, embeddings, Retrieval Augmented Generation (RAG), and foundational model evaluation approaches
- 2+ years of experience building and deploying cloud-native applications on Azure (or equivalent platforms), including CI/CD and deployment processes
Preferred Qualification:- Familiarity with the Hugging Face ecosystem, including model usage, basic fine-tuning, and inference optimization techniques
- Strong foundation in data science concepts, including statistics, probability, and experimentation, with the ability to apply these principles to real-world problems
- Strong software engineering fundamentals, including Git-based version control, CI/CD pipelines, code reviews, testing strategies, and secure coding practices
- Proven ability to collaborate effectively with engineering, product, QA, and operations teams in an Agile development environment
- Proven ability to collaborate effectively with engineering, product, QA, and operations teams in an Agile development environment
- Experience with advanced LLM techniques, including prompt engineering optimization, fine-tuning strategies, and evaluation frameworks
- Exposure to LLMOps practices, such as model/prompt versioning, monitoring, drift detection, and cost optimization
- Experience working with large-scale healthcare or quality datasets or other highly regulated domains
- Familiarity with performance tuning and optimization for AI/ML workloads and distributed systems
- Exposure to experiment tracking, A/B testing frameworks, and model performance monitoring
- Experience contributing to technical design discussions and implementing scalable system components based on architectural guidance
- Demonstrated ability to mentor junior engineers, participate in code reviews, and contribute to improving team engineering practices
*All Telecommuters will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $91,700 - $163,700 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
