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Databricks

Builds cloud software for data analysis and AI

Lead Solutions Architect - Generative AI, Emerging Digital Natives and Startups

Pre-Sales Solution EngineeringConfirmed live in the last 24 hours
Full-timeRemote in FranceNo salary listedRemote

About the Job

Requirements

  • Strong understanding of leading proprietary and open-source large language models and providers, their capabilities, trade-offs, and suitability for different use cases, and ability to present a clear point of view to customers.
  • Deep expertise in generative AI patterns including retrieval-augmented generation, agents, tool use, fine-tuning, model customization, prompt engineering, evaluation engineering, and large language model guardrails and safety.
  • Strong foundations in machine learning and deep learning, including distributed training, GPU workloads, and the MLOps lifecycle of tracking, registry, serving, and monitoring.
  • Proficiency in Python and the machine learning ecosystem, including PyTorch, Transformers, MLflow, and Spark, with experience building production-quality reference implementations.
  • Experience with the Databricks platform or ability to learn it quickly, including Mosaic AI, Model Serving, Vector Search, and Unity Catalog; cloud experience with AWS, Azure, or GCP.
  • Ability to earn the trust of engineers and senior technical executives and explain complex generative AI trade-offs clearly.
  • A track record of technical leadership and mentorship.
  • Comfort operating at the pace of digital-native and high-growth customers.
  • Based in the United Kingdom, able to work across EMEA time zones, and willing to travel to customer sites in the region as needed.

Responsibilities

  • Serve as the deep technical authority on generative AI, large language models, and applied machine learning for the EMEA Emerging Digital Natives and Startups business unit, supporting strategic and complex customer engagements.
  • Partner with Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk generative AI use cases, including retrieval-augmented generation, agentic systems, fine-tuning, and evaluation.
  • Build hands-on proofs of concept and reference implementations that operationalize large-scale large language model and deep learning workloads on Databricks, using Mosaic AI, MLflow, Model Serving, Vector Search, and Unity Catalog governance.
  • Lead fine-tuning and model-customization engagements on open large language models, including judge-based and label-efficient evaluation approaches for domains where quality and safety are paramount.
  • Channel field and customer feedback into the product roadmap and represent the roadmap back to the field.
  • Enable and mentor the broader EMEA Emerging Digital Natives and Startups Field Engineering team through workshops, reference architectures, and internal enablement.
  • Represent Databricks externally through conference talks, blogs, and customer executive briefings.

Desired Qualifications

  • Background helping digital-native customers build data and machine learning solutions, such as prior experience at a data or machine learning platform or consulting vendor.
  • Public technical presence through conference speaking, published talks, blogs, or open-source contributions in machine learning or generative AI.
  • Domain depth in a regulated or high-stakes industry such as healthcare, life sciences, or financial services, where model quality, evaluation, and safety are critical.
  • Experience partnering with Product and Engineering teams to influence a roadmap.

About the Company

Databricks

Builds cloud software for data analysis and AI

Databricks builds cloud software that businesses use to organize data, analyze it, and develop AI applications. Its lakehouse architecture brings the flexible storage of a data lake together with the querying and management tools of a data warehouse. Data engineers prepare and update datasets, analysts build reports, and developers train models or create applications in a shared workspace with controls over who can use the data. The platform runs on Amazon, Microsoft, and Google clouds, and customers pay for computing usage or commit to longer contracts for discounted rates. Databricks was founded in 2013 and is headquartered in San Francisco, California.

Company size
10,001+
Company stage
Late Stage VC
Total funding
$32.1B
Headquarters
San Francisco, CA
Founded
2013

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