Lead Solutions Architect - Generative AI (EMEA Emerging DNB)Active
The opportunity
We are looking for a Lead Solution Architect focused on Generative AI to support our EMEA Emerging Digital Natives and Startups business unit — one of the fastest-moving, most technically ambitious patches in EMEA. Our customers are digital-native and cloud-native companies who…
What you'll do
Serve as the deep technical authority on Generative AI, LLMs, and applied: machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts.
Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers,: and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation.
Build hands-on proofs of concept and reference implementations that: operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect.
Lead fine-tuning and model-customisation engagements on open LLMs (e.g.,: Llama-family models), including judge-based and label-efficient evaluation approaches for domains where quality and safety are paramount.
Act as a bridge to Product and Engineering: channel field and customer feedback into the roadmap and represent the roadmap back to the field.
Enable and mentor the broader EMEA Emerging DNB Field Engineering team: through workshops, reference architectures, and internal enablement, multiplying GenAI expertise across our ~70-person SA/SE organisation.
What they're looking for
- Strong understanding of the LLM landscape, including leading proprietary and: open-source models and providers, with the ability to differentiate their capabilities, trade-offs, and suitability for different use cases, and to articulate a clear, informed point of view (POV) to customers.
- Deep expertise in the modern GenAI stack: LLM application patterns (RAG, agents, tool use), fine-tuning and model customization, prompt and evaluation engineering, and LLM guardrails/safety.
- Strong foundations in machine learning and deep learning, including: distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring).
- Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow,: Spark), and comfort building production-quality reference implementations.
- Experience with the Databricks platform: or the ability to ramp on it quickly — including Mosaic AI, Model Serving, Vector Search, and Unity Catalog. Cloud experience across AWS, Azure, or GCP.
- Excellent communication and consultative skills: able to earn the trust of both hands-on engineers and senior technical executives, and to explain complex GenAI trade-offs clearly.
- A track record of technical leadership and mentorship: someone who elevates the people around them.
- Comfort operating at the pace of digital-native and high-growth customers,: where speed, iteration, and technical credibility win the deal.
