Senior Specialist Solutions Architect (AI/ML)

As a Senior Specialist Solutions Architect (ML & AI), you will serve as the trusted technical ML and AI expert for Databricks customers and the Field Engineering organization. You will partner with Solution Architects to guide enterprise and strategic customers in architecting production-grade ML and AI applications on the Databricks Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting-edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI  thought leader.

• Architecting Workloads: Design and implement production-level ML and AI workloads, including end-to-end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud-native services.
• GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool-calling, multi-agent orchestration, and guardrails), AI observability, and natural language querying of structured data.
• Provide advanced technical support to Solution Architects during the technical sales cycle by building MVPs, leading deep-dive sessions, and aligning AI solutions with complex customer business challenges.
• Product Influence: Collaborate cross-functionally with product and engineering teams to represent the voice of the customer, define priorities, and influence the platform’s AI roadmap.
• Thought Leadership: Drive community growth and AI platform adoption through the creation of technical tutorials and training materials, as well as by presenting at industry conferences and leading hackathons.

• Experience: 10+ years of hands-on industry DS/ML experience, with a focus on either:
• ML Engineering: Building/maintaining production-grade cloud infrastructure (AWS/Azure/GCP) that supports deployment of ML applications and monitoring ML model performance.
• Data Science/AI: Applying advanced techniques in LLMs, agentic systems, vector databases, fine-tuning, and deployment tools (e.g., HuggingFace, Langchain).
• Hands-on experience working with Distributed Spark based systems
• Experience with data engineering concepts or a good understanding of data engineering concepts 
• Pre-sales or post-sales experience working with external clients across a variety of industry markets. Minimum of 5+ years of customer-facing experience would be preferred
• [Preferred] Experience working with Apache Spark™ to process large-scale distributed datasets
• Communication: Proven ability to communicate and teach complex technical concepts to both technical and non-technical audiences.
• Core Traits: Passion for lifelong learning, collaboration, and driving business value through AI.
• Education: Graduate degree in a quantitative discipline (e.g., Computer Science, Engineering, Statistics, Operations Research, etc) or equivalent practical experience.
• Can meet expectations for technical training and role-specific outcomes within 3 months of hire
• Can travel up to 30% when needed

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.