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Databricks

Builds cloud software for data analysis and AI

Lead Solutions Architect - Generative AI, Emerging DNB

Pre-Sales Solution EngineeringConfirmed live in the last 24 hours
Full-timeRemote in UKNo salary listed

About the Job

Requirements

  • Strong understanding of the large language model landscape, including proprietary and open-source models and providers, their capabilities and trade-offs, and suitability for use cases.
  • Deep expertise in generative AI patterns and practices, including retrieval-augmented generation, agents, tool use, fine-tuning, model customization, prompt and evaluation engineering, and 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 the ability to build 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 work across EMEA time zones and willingness to travel to customer sites within the region as needed.
  • Ability to earn the trust of hands-on engineers and senior technical executives and explain complex generative AI trade-offs clearly.

Responsibilities

  • Serve as the deep technical authority on generative AI, large language models, and applied machine learning for the EMEA Emerging DNB business unit, supporting strategic and complex customer engagements.
  • Partner with EMEA Emerging DNB 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 to operationalize large-scale language-model and deep-learning workloads on Databricks, including Mosaic AI, MLflow, Model Serving, Vector Search, and Unity Catalog governance.
  • Lead fine-tuning and model-customization engagements on open language models, including judge-based and label-efficient evaluation approaches for domains where quality and safety are paramount.
  • Channel field and customer feedback to Product and Engineering and represent the product roadmap to the field.
  • Enable and mentor the broader EMEA Emerging DNB 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 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 the 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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