Chief GenAI 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.

Salary information – CalHR salary rules allow appointment at the entrance rate (Cal. Code Regs., tit. 2, § 599.673) of a classification. For classes with alternate ranges, placement is based on education/experience.

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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.