AI/ML Engineer
About The Position
The AI/ML Engineer will design, build, and operationalize AI capabilities for the Enterprise Finance organization to support decision-making, productivity, and innovation. The role involves developing AI-powered solutions such as large language model applications, retrieval-augmented generation workflows, intelligent agents, classification models, summarization workflows, and decision-support tools. The engineer will work with Finance, Data Engineering, Architecture, Security, and Responsible AI teams to deliver trusted, scalable, governed solutions aligned with enterprise AI/ML engineering practices and AI productization standards. The position is remote within the United States, subject to UnitedHealth Group’s Telecommuter Policy.
Requirements
- At least 2 years of experience in software engineering, machine learning, or AI engineering.
- At least 1 year of experience with cloud-native architecture, APIs, and scalable software systems.
Nice To Haves
- Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
- Experience supporting Finance, FP&A, Accounting, Treasury, Tax, or related enterprise corporate functions.
- Experience developing and deploying agentic AI solutions.
- Familiarity with AI observability, model monitoring, vector databases, semantic search, and knowledge retrieval systems.
- Experience developing enterprise AI products from conceptual design through production support.
- At least 1 year of hands-on experience building and deploying generative AI and LLM-based applications in production environments.
- At least 1 year of experience with prompt engineering, RAG architectures, AI agents, and model evaluation.
- At least 1 year of experience applying Responsible AI, security, privacy, or compliance practices within engineering deliverables.
Responsibilities
- Design, develop, and deploy AI-powered solutions and architectures, including LLM workflows, prompt orchestration chains, agent-based solutions, RAG applications, and decision-support capabilities.
- Develop prompt strategies, grounding approaches, tool-calling patterns, guardrails, and fallback mechanisms to improve solution quality, safety, and reliability.
- Partner with Data Engineering teams to ensure access to trusted, governed, and high-quality enterprise data sources.
- Establish evaluation frameworks, test datasets, scoring rubrics, and model-performance benchmarks across AI deliverables.
- Monitor and optimize output quality, relevance, accuracy, consistency, latency, cost efficiency, and hallucination risk in production systems.
- Conduct failure-mode analysis, optimize model performance, and implement continuous improvements to AI systems.
- Collaborate with Quality Engineering, Responsible AI, Security, Privacy, Compliance, and Enterprise Architecture teams.
- Document AI solution behavior, limitations, risk controls, and operational monitoring requirements.
- Support production readiness through observability, monitoring, model evaluation, and drift detection practices.
- Evaluate emerging AI trends and leverage enterprise-approved AI tools to inform solution design, streamline workflows, and drive innovation.
Benefits
- Comprehensive benefits package
- Incentive and recognition programs
- Equity stock purchase
- 401(k) contribution
- Career development opportunities
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
