Specialist Solutions Architect - AI/ML

About the Role

As an AI/ML Specialist Solutions Architect (SSA), you will lead the advanced AI/ML technical strategy for your customers — owning complex architecture discussions, driving platform adoption, and serving as a trusted advisor to customer technical leads and architects. You combine deep technical expertise with strategic thinking to position Databricks as the foundation of your customers' data and AI strategy.

The Impact You Will Have

• Own the end-to-end AI/ML technical strategy for your accounts, from discovery through production deployment and consumption growth.
• Lead complex architecture discussions — designing scalable, production-grade solutions spanning AI/ML, including Retrieval-Augmented Generation (RAG), tool calling, multi-agent orchestration, guardrails, AI evaluation, and observability systems.
• Serve as a trusted technical advisor to customer architects, engineering leads, and Directors.
• Drive technical wins in competitive scenarios by demonstrating Databricks' differentiation through custom-built solutions.
• Develop and declare an emerging technical specialization (archetype) — becoming a go-to resource for your team in that domain.
• Orchestrate cross-functional resources (DSAs, SAs, Partners) to deliver comprehensive solutions for complex customer needs.
• Influence product direction by providing structured feedback on customer requirements and competitive gaps.

What We Look For

• 6+ years in solutions architecture, technical pre-sales, or a senior hands-on technical role in ML Engineering and AI Engineering.
• ML Engineering: Building and maintaining cloud infrastructure (AWS, Azure, or GCP) supporting production ML applications and drift monitoring.
• AI Engineering: Working with LLMs and agentic systems, including vector databases, fine-tuning, AI guardrails, and frameworks like LangChain, Hugging Face, or OpenAI APIs.
• Strong coding proficiency in Python and SQL (live coding, debugging, and solution-building).
• Deep expertise in distributed data systems architecture: designing scalable pipelines, streaming architectures, lakehouse patterns, and cloud-native data platforms.
• Proficient on the Databricks Platform (or demonstrated ability to achieve proficiency rapidly) with a developing technical specialization.
• Proven ability to lead architecture discussions with senior technical stakeholders (whiteboarding, design reviews, trade-off analysis).
• Experience with production deployments on public cloud (AWS, Azure, or GCP), including infrastructure, security, and governance considerations.
• Track record of driving platform adoption and consumption growth within accounts.
• Excellent communication skills.
• Bachelor's or Master's degree in Computer Science, Engineering, or a quantitative discipline (or equivalent experience).
• Willingness to travel up to 30% as needed.

Nice to Have

• Databricks certifications (Data Engineer, ML, Platform).
• Experience with competitive platforms (Snowflake, AWS native services, Azure Synapse).
• Background in a data/AI company or cloud provider.
• Industry domain expertise (Financial Services, Healthcare, Retail, Media, etc.).