Lead AI Insights Engineer

Candidates must be legally authorized to work in the United States without employer sponsorship, now or in the future.

As an AI Insights Engineer at agilon health, you will deliver production-grade AI products within agilon’s Tech Group. Key areas of development include unstructured data processing, clinical suspect identification, and clinical risk models. In this role, you will develop and deploy clinically appropriate AI products using LLMs, NLP, and ML tools. You will work closely with stakeholders across the organization to understand the clinical, business, and technical requirements, and be responsible for communicating the impact that AI products create at agilon.

*Only candidates with healthcare experience will be considered.

Essential Job Functions:

• Leverage LLM tools to drive early disease identification and clinical recommendations through agilon’s unstructured clinical data, including notes and images.
• Create production-grade AI products, including the supporting data pipelines, monitoring, and performance analysis.
• Communicate the value and opportunities of AI to internal stakeholders.
• Collaborate with Market Operations, Insights Engineering, and Clinical teams to drive real-world outcomes.
• Contribute to the development of our AI products in a fast-paced, agile environment.

Required Qualifications:

Minimum Experience

• 7+ Years of experience in a related field, including Analytics, Data Science, or Data Engineering.
• 3+ Years in a healthcare setting, preferably value-based care.

Education/Licensure

• Bachelor’s and/or master’s in a quantitative field, such as Computer Science, Data Science, or Mathematics.

Knowledge, Skills, and Abilities:

• Strong SQL skills and experience (prefer Snowflake + dbt) with data visualization tools (prefer Sigma).
• Advanced knowledge of at least one programming language outside of SQL (prefer python).
• Experience with cloud data engineering and AI infrastructure tools, such as Snowflake Cortex, dbt, AWS Sagemaker/Bedrock, etc.
• Experience with AI-enabled development practices and tools (Claude Code, Cursor, etc.), including MCP server development and context engineering best practices.
• Deep knowledge of data sources and common analytical methods in healthcare, including FHIR sources, claims, pharmacy, and lab data.
• Understanding of value-based care concepts in the US, including Medicare payment models, medical margin, and quality of care.
• Intellectual curiosity and a passion for using data to solve real-world problems at scale.
• Excellent communication skills and the ability to proactively explain complex concepts to non-technical stakeholders.
• Enthusiasm for learning and adapting in a dynamic environment.

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Salary range shown is a guideline. Individual compensation packages can vary based on factors unique to each candidate, such as skill set, experience, and qualifications.