Lead Solutions Architect - Generative AI (EMEA Emerging DNB)

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En corto

→Arquitecto técnico líder en IA generativa para startups digitales en EMEA.
→Diseñas y pruebas prototipos de IA generativa en producción usando Databricks, con foco en RAG, fine-tuning y seguridad.
→Destaca por liderar la innovación técnica y capacitar al equipo de campo en IA generativa.

Fluent English required for customer and internal collaboration.

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¿Qué piden?

✓Experto en LLMs y arquitecturas de IA generativa (RAG, agentes, fine-tuning).
✓Experiencia comprobada con Python, PyTorch, Transformers y MLflow.
✓Conocimiento profundo de Databricks: Mosaic AI, Vector Search, Model Serving, Unity Catalog.
✓Capacidad para liderar pruebas de concepto y arquitecturas de producción.
✓Habilidades excepcionales de comunicación para ingenieros y ejecutivos.
✓Experiencia en entornos cloud (AWS, Azure o GCP) y entornos de MLOps.

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PythonPyTorchTransformersMLflowMosaic AIModel ServingVector SearchUnity CatalogSparkAWS

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REQ ID: FEQ227R147 Location: United Kingdom (Remote/Hybrid — London or UK-based) Recruiter: Dina Hussain About the Role 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 build on the frontier: they adopt GenAI early, scale fast, and expect their technical partners to be as sharp as their own engineers. You will be the go-to expert our Account Teams and customers turn to when a GenAI or LLM use case needs to move from an ambitious idea to a production-grade reality across our high-growth accounts. This is a highly technical, customer-facing individual contributor role for someone who lives at the frontier of applied GenAI. You will shape architectures, prove out the hard problems, challenge and influence customer roadmaps, and raise the GenAI capability of the entire EMEA Emerging DNB field organisation. 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. • Represent Databricks externally as a technical thought leader — conference talks (e.g., Data + AI Summit), blogs, and customer executive briefings. What We'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 ex

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