Sr. Developer Advocate, Databricks AI Agentic Systems

Sr. Developer Advocate, Databricks AI Agentic Systems

Bellevue, Washington

Are you a recognized technical leader in Generative AI and MLOps, driven to define the future of production AI Agentic Systems? This Senior Developer Advocate role is a high-level position that grants strategic ownership over developer adoption and technical discourse surrounding Agent Bricks on the Databricks Data Intelligence Platform.

As a crucial link between our engineering teams and the global developer community, you will accelerate the careers of data scientists and AI engineers by coalescing advanced research, customer learnings, and best practices into scalable, production-ready reference implementations, presentations, and demos. You will be instrumental in cultivating the global community for AI Agentic workflows, LLMOps, with particular focus on MLflow, and Agentic System governance.

At Databricks, we are passionate about enabling data and AI teams to solve the world’s most challenging problems. Our mission in Developer Relations (Dev Rel) is to empower data practitioners, data scientists, and the broader developer ecosystem by cultivating vibrant communities, crafting exceptional content, and fostering a truly reciprocal relationship with our users. Our primary goal is to drive awareness and adoption of the Databricks Data Intelligence Platform.

You will leverage your technical depth, community-building skills, and market knowledge to drive awareness and adoption, positioning Databricks as the definitive technical leader in enterprise AI governance and Agentic Systems.

• Strategic Roadmap Ownership: Define and execute the global technical advocacy strategy and roadmap for a critical pillar of Databricks AI Agentic Systems (e.g., RAG Architectures, AI Agent Evaluation, or LLM Governance), ensuring alignment with product goals and quantifiable metrics.
• Evangelism: Work with the field AI engineers to design and deploy production-grade reference implementations and create high-impact live demonstrations (demos) that solve real-world enterprise GenAI challenges, showcasing best practices in performance, evaluation, and security. You will evangelize Agent Bricks as the definitive way to "
  Take your AI to your Data
  ".
• Technical Content Scaling: Create high-quality, actionable educational resources, including comprehensive courseware,
  advanced tutorials, technical blog posts, and video content
  . This content will focus on accelerating the end-to-end AI Agentic workflows and LLMOps lifecycle, including vectorization, prompt engineering, and the use of MLflow, Agent Evaluation, and Agent Feedback for continuous refinement.
• Product Influence and Advocacy: Serve as the primary internal technical advocate for the global AI/ML community, translating complex friction points in the developer experience into actionable engineering specifications and influencing the product direction of Agentic Systems.
• Community Governance and Growth: Speak and build community by expanding and governing the MLOps and LLMOps communities (including MLflow meetups),
  mentoring new contributors
  , and enabling data teams to leverage tools for building MLOps/LLMOps infrastructure. You will establish thought leadership by articulating complex concepts, such as AI Guardrails, lineage tracking, and security controls, within RAG applications, ensuring that Agents are built on governed data.

• Experience and Tenure: 5+ years of total technical experience
  , including a minimum of 3 years in a dedicated Developer Advocate, Developer Evangelist, Solution Architect, Data Scientist, or AI Field Engineering role.
• Production Agentic Systems Track Record: A proven history of building, deploying, and operating production-grade AI Agentic Systems
  . Expertise in designing robust AI system interactions, implementing RAG chains, and developing autonomous agent applications using the Agent Bricks workflow
  .
• Deep Technical Proficiency:
  Expert-level knowledge of Python, major machine learning frameworks (e.g., PyTorch, scikit-learn), and modern LLMOps orchestration tools (e.g., Lang Chain, Llama Index,…