Design and implement production ML and AI workloads, including end-to-end pipelines
Architect RAG, agentic and GenAI solutions for enterprise customers
Build MVPs and lead deep-dive sessions during the technical sales cycle
Create tutorials and training materials and present at conferences
Feed customer priorities into the platform's AI product roadmap
From the employer’s posting
Design and implement production-level ML and AI workloads, including end-to-end pipelines
Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems
building MVPs, leading deep-dive sessions
creation of technical tutorials and training materials, as well as by presenting at industry conferences
represent the voice of the customer, define priorities, and influence the platform’s AI roadmap
About Databricks
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.
Open 4 monthsWe first saw this posting 124 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
Status in our records
Live
First seen by us
Jul 12, 2026124 days ago
Recorded sightings
275
Last seen by us
Nov 13, 2026
Jul 12, 2026today
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 124
477
Up longer than most. Similar jobs usually come down between day 4 and day 77.
At Databricks, jobs usually come down by about day 63.
From 51 similar postings that came down, and 19 of Databricks’s. Coming down is not always a hire.
Databricks is hiring
29 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
AWS
Azure
Databricks
GCP
Spark
Hugging Face
Where the posting names them
- 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).
Mission
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.
Impact you will have
- Can travel up to 30% when needed
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.
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
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
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
- 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.
- 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
Complete your application on databricks.com. The employer’s form will show what is required.
Applying through Greenhouse
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Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
August 17Last week
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Skills: Data Science, Machine Learning, AI, LLM, GenAI
Mission
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.
Impact you will have
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.
What we look for
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
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.