Chief AI Engineer

• →Build smart applications: Design and deploy intelligent applications that use the right tool for the job—whether that is Retrieval-Augmented Generation (RAG), fine-tuning, or other grounding techniques—to ensure results are accurate and context-aware.
• →Turn messy text into insights: Engineer pipelines that take unstructured documents and turn them into structured, useful data.
• →Automate with agents: Create autonomous workflows that can retrieve information and assist humans with complex analysis, all while keeping safety guardrails in place.
• →Champion safe AI: Act as the technical conscience of the team, ensuring every system we build is ethical, secure, unbiased, and privacy-first.
• →Scale and operationalize: Take 'proof of concepts' and turn them into monitored production services using modern DevOps and MLOps practices.
• →Teach and mentor: Run workshops, mentor data professionals, and help upskill the state workforce on the latest AI engineering practices.

ODI staff reside throughout California. Travel to the Sacramento headquarters may be required as needed. This position provides telework opportunities in accordance with agency telework policies.

ODI encourages people with disabilities to apply for jobs with us.

You will find additional information about the job in the Duty Statement.

We encourage you to apply regardless of whether you think you meet 100% of the desirable qualifications.

• Applied AI Experience: Deep experience building and deploying LLM-backed applications, using techniques like RAG, vector search, or agentic orchestration.
• Cloud Fluency: Advanced proficiency in engineering within major cloud environments (AWS, GCP, Azure, or Snowflake) and deploying secure cloud infrastructure.
• Strong Coding Chops: Expert-level Python skills, with a focus on writing production-quality, testable, and maintainable code.
• MLOps Knowledge: Experience setting up CI/CD pipelines for machine learning to automate testing and deployment.
• Communication Skills: The ability to explain complex technical concepts to non-technical stakeholders and translate policy requirements into code.
• A 'Responsible AI' Mindset: A proven track record of implementing guardrails, bias detection, and privacy protection in AI systems.