Tech Stack

Description

You will serve as the bridge between platform engineers and data scientists on the Agent Platform Team, testing and evaluating new Databricks features and integrations, documenting findings, and shaping the platform roadmap. You'll gather practitioner needs, write guides, conduct training, and drive adoption of AI/ML capabilities across the organization.

Requirements

• Bachelor's degree in mathematics, statistics, computer science, economics, or a related discipline.
• 2-4 years of experience in data science, AI/ML development, or applied analytics.
• 2-4 years of experience in tech consulting, retail analytics, or related professional services.
• Hands-on experience with Python, SQL, PySpark and Databricks.
• Experience gathering user needs, documenting requirements, and driving issues to resolution with technical teams.
• Experience partnering with software engineering and/or product management teams.
• Familiarity with LLMs, prompt engineering, or agentic AI concepts is a strong plus.
• Experience with the Databricks ecosystem (Unity Catalog, MLflow) is a strong plus.
• Exposure to agent frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen) is a plus.
• Strong written and verbal communication skills.
• Ability to influence across teams and seniority levels without direct authority.
• Comfort operating in ambiguity.
• Strong project and time management skills.
• Curiosity and enthusiasm for emerging AI technologies.

Responsibilities

• Voice of the practitioner: Serve as the primary liaison between data scientists using Databricks and the platform engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback.
• Technology evaluation: Lead the testing and evaluation of new Databricks capabilities and third-party tools. Document findings, recommended use cases, limitations, and rollout considerations.
• Onboarding enablement: Write guides, maintain documentation, lead training sessions, and run office hours to help data scientists adopt new platform capabilities.
• Adoption communication: Evangelize Databricks capabilities to increase awareness and drive adoption. Produce clear release notes, communications, and materials.
• Cross-functional collaboration: Work closely with the solution architect, cloud engineer, and product senior on the squad to ensure proposed technologies have well-defined use cases and guidelines.
• Governance compliance support: Help validate that new platform capabilities meet enterprise requirements around security, compliance, observability, and data governance.