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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. You are further developing a technical specialization and are recognized within the Field Engineering team for depth in the specific domain.
This position can be remote.
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 the following areas:
- 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 — you must demonstrate live coding, debugging, and solution-building skills
- 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 in one area (e.g., real-time/streaming, ML/AI, data governance, migrations)
- Proven ability to lead architecture discussions with senior technical stakeholders — whiteboarding, design reviews, and 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 — able to translate complex architectures into business value for both technical and executive audiences
- 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) — understanding the landscape you'll position against
- Background in a data/AI company or cloud provider
- Industry domain expertise (Financial Services, Healthcare, Retail, Media, etc.)
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
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.
Our Commitment to Diversity and Inclusion
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.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Skills Required
- 5+ years hands-on industry experience in ML Engineering or AI Engineering (cloud ML infrastructure, drift monitoring)
- Experience working with LLMs and agentic systems, including vector databases, fine-tuning, and frameworks like LangChain, Hugging Face, or OpenAI APIs
- Designing and deploying production ML/AI architectures using the Databricks unified platform
- Understanding of modern lakehouse architectures (Delta Lake, data modeling, BI integration) across AWS, Azure, or GCP
- Ability to translate complex AI/ML concepts for technical and non-technical audiences
- Graduate degree in Computer Science, Engineering, Statistics, or related field, or equivalent practical experience
- Prior experience in a pre-sales or post-sales technical consulting role
- Ability to travel up to 30% for customer engagements
- Ability to hit role-specific training and technical delivery milestones within the first 6 months
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Healthcare Strength — Company materials highlight comprehensive medical, dental, and vision coverage alongside mental-health resources, wellness reimbursements, and business travel insurance. Offerings are described as broad and modern, with core health coverage consistently emphasized.
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Parental & Family Support — Paid parental leave is explicitly called out, with details such as up to 20 weeks for birthing parents and up to 12 weeks for non-birthing parents in the U.S. Public materials also reference family-forming support, reinforcing the focus on families.
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Wellbeing & Lifestyle Benefits — Wellness programs and perks include gym reimbursement, periodic wellness events (e.g., yoga, massages), and in-office meals and snacks in many locations. Personal development funds and discounts further enhance lifestyle and growth support.
Databricks Insights
What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
