Lead Solutions Architect - Generative AI (EMEA Emerging DNB)

Role overview

A remote UK-based lead architect role focused on Generative AI for an EMEA team serving digital-native and born-in-the-cloud customers. This is a highly technical, customer-facing individual contributor position that shapes architectures for LLM and GenAI use cases across high-growth accounts, while raising the GenAI capability of the wider field organisation.

Responsibilities

- Act as the deep technical authority on generative AI, LLMs, and applied ML for strategic customer engagements across digital-native accounts. - Partner with solutions architects, engineers, and account teams to scope, design, and de-risk GenAI use cases spanning retrieval-augmented generation, agentic systems, fine-tuning, and evaluation. - Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on a unified data and AI platform (model serving, vector search, governance, ML lifecycle tooling), tuned to fast customer iteration cycles. - Lead fine-tuning and model-customisation engagements on open LLMs, including judge-based and label-efficient evaluation for domains where quality and safety are paramount. - Channel field and customer feedback into the product roadmap and represent roadmap direction back to the field teams. - Enable and mentor the broader field engineering organisation through workshops, reference architectures, and internal enablement programmes. - Represent the organisation externally through conference talks, technical blogs, and customer executive briefings.

Requirements

- Strong understanding of the LLM landscape, including leading proprietary and open-source models, with the ability to differentiate capabilities and articulate a clear point of view. - Deep expertise in modern GenAI application patterns: RAG, agents, tool use, fine-tuning, prompt and evaluation engineering, and LLM guardrails and safety. - Solid 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), with comfort building production-grade reference implementations. - Hands-on experience with a unified data and AI platform covering model serving, vector search, and governance, plus cloud experience across AWS, Azure, or GCP. - Excellent communication and consultative skills, able to earn the trust of both hands-on engineers and senior executives. - Track record of technical leadership and mentorship, comfortable operating at the pace of digital-native customers. - Based in the United Kingdom, able to support EMEA time zones and travel within the region.

Nice to have

- Public technical presence through conference speaking, blogs, or open-source contributions in ML/GenAI. - Domain depth in a regulated industry such as healthcare/life sciences or financial services. - Experience partnering with product and engineering to influence roadmap direction.

Benefits and work setup

- Remote or hybrid working based in the United Kingdom with opportunities to engage EMEA customers on site. - Inclusive hiring practices across protected characteristics, with equal employment opportunity standards.