Sr. Specialist Solutions Architect (AI/ML)
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will be the go-to technical expert for Databricks' AI platform, helping customers move from AI experiments to governed, cost-efficient agents and ML systems. You will work with Account Executives and Solutions Architects to win strategic opportunities, design architectures, lead hands-on proofs of concept and MVPs, and advise customer technical leaders. You will also shape the AI go-to-market in the region, enable the field team, and feed customer learnings back to Product and Engineering.
Requirements
- 7+ years of hands-on experience in machine learning, AI engineering or data science, including customer-facing technical work
- Track record of shipping LLM and agent systems to production
- Deep knowledge of agent architecture
- Practical experience evaluating GenAI systems
- Hands-on with at least one modern agent framework (e.g. LangGraph, OpenAI Agents SDK, Google ADK, DSPy)
- Understanding of the foundation-model landscape and trade-offs
- Strong grounding in classical ML and MLOps
- Strong Python and SQL skills
- Experience with at least one major cloud platform (AWS, Azure or GCP)
- Ability to influence technical leaders up to CTO/CDO level
- Ability to communicate and teach complex technical concepts
Responsibilities
- Lead discovery, architecture design, demos, proofs of concept and MVPs for agent, GenAI and ML use cases
- Architect agents and multi-agent applications using Agent Bricks, Databricks Apps, MCP tools, Lakebase, AI Search and Foundation Model APIs
- Govern assets under Unity Catalog
- Build evaluation and observability into customer systems using MLflow tracing, LLM judges, evaluation datasets, release gates and monitoring
- Position the AI Gateway for model access, guardrails, PII protection, usage tracking and rate limiting
- Enable SAs and partners with workshops, reusable assets and reference architectures
- Bring customer feedback to Product Management and Engineering
- Represent Databricks at executive briefings, meetups, conferences and in written content