FEQ228R125

As a Senior Specialist Solutions Architect (Sr. SSA) for AI/ML, you will be the go-to technical expert for Databricks' AI platform across our customers. You will help them move from AI experiments to governed, cost-efficient agents and ML systems running on the Databricks Data Intelligence Platform.

You will work with Account Executives and Solutions Architects to win strategic opportunities. You will design architectures for advanced agent and ML use cases and lead hands-on proofs of concept and MVPs. You will act as a trusted advisor to customer technical leaders on AI strategy, governance and economics. You will also shape our AI go-to-market in the region, enable the wider field team, and feed what you learn from customers back to Product and Engineering.

This role reports to the Manager, Specialist Solutions Architects – Switzerland, Austria & Eastern Europe.

The impact you will have

• Win the technical decision for AI on Databricks. Lead discovery, architecture design, demos, proofs of concept and MVPs for agent, GenAI and ML use cases in competitive evaluations.
• Design production-grade agent systems. Architect agents and multi-agent applications using Agent Bricks, Databricks Apps, MCP tools, Lakebase, AI Search and the Foundation Model APIs. Govern assets under Unity Catalog
• Make agent quality measurable. Help customers build evaluation and observability into everything they ship, using MLflow tracing, domain-specific LLM judges, labelled evaluation datasets, release gates and production monitoring.
• Govern AI at enterprise scale. Position the AI Gateway as the central control point for model access, guardrails, PII protection, usage tracking and rate limiting.
• Scale your expertise. Enable SAs and partners with workshops, reusable assets and reference architectures. 
• Influence the product. Bring structured customer feedback and requirements specific to the market to Product Management and Engineering.
• Be a visible voice. Represent Databricks at customer executive briefings, meetups, conferences and in written content.

What we look for

• 7+ years of hands-on experience in machine learning, AI engineering or data science, including customer-facing technical work (pre-sales, consulting or technical leadership).
• A track record of shipping LLM and agent systems to production, not just prototypes, including evaluation, monitoring and cost management.
• Deep knowledge of agent architecture: tool use and function calling, MCP, agent memory and state, orchestrating multiple agents, and retrieval and context design.
• Strong practical experience evaluating GenAI systems: tracing, LLM-as-a-judge, building evaluation datasets, regression testing and observability in production.
• Hands-on with at least one modern agent framework (e.g. LangGraph, OpenAI Agents SDK, Google ADK, DSPy). You should be framework-agnostic in how you design.
• Solid understanding of the foundation-model landscape (e.g. Anthropic, OpenAI, Google, open-weight models) and the trade-offs between quality, latency, cost and data residency.
• Strong grounding in classical ML and MLOps: model development, deployment, monitoring and lifecycle management at scale.
• Strong software engineering skills in Python and SQL. You use AI coding assistants (e.g. Claude Code, Cursor) daily and can build a working demo application quickly.
• Experience with at least one major cloud platform (AWS, Azure or GCP) and modern data platforms.
• Proven ability to influence technical leaders up to CTO/CDO level and to explain complex AI topics in business terms.

• Communication: Proven ability to communicate and teach complex technical concepts to both technical and non-technical audiences.