Lead Solutions Architect - Generative AI (EMEA Emerging DNB)
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will be the go-to GenAI expert for account teams and digital-native customers, helping move LLM use cases from idea to production. You will design architectures, build proofs of concept on Databricks, lead fine-tuning engagements, mentor the field team, feed customer insights to Product and Engineering, and represent the company externally through talks and blogs.
Requirements
- Strong understanding of the LLM landscape, proprietary and open-source models
- Deep expertise in the GenAI stack: RAG, agents, tool use, fine-tuning, evaluation, guardrails
- Strong foundations in machine learning, deep learning, distributed training, GPU workloads and MLOps
- Proficiency in Python, PyTorch/Transformers, MLflow, Spark
- Cloud experience across AWS, Azure or GCP
- Excellent communication and consultative skills
- Track record of technical leadership and mentorship
- Based in the United Kingdom and able to work across EMEA time zones, willing to travel
Responsibilities
- Serve as the deep technical authority on Generative AI, LLMs and applied machine learning
- Partner with Solutions Architects and Account teams to scope, design and de-risk GenAI use cases
- Build proofs of concept and reference implementations for LLM and deep learning workloads on Databricks
- Lead fine-tuning and model-customisation engagements on open LLMs
- Channel field and customer feedback to Product and Engineering
- Enable and mentor the Field Engineering team through workshops and reference architectures
- Represent Databricks externally via conference talks, blogs and executive briefings