Lead Solutions Architect - Generative AI (EMEA Emerging DNB)

Role overview

Lead customer-facing Generative AI engagements for high-growth, digital-native businesses across EMEA. As a hands-on technical advisor, turn emerging LLM and machine-learning ideas into production-ready architectures and working implementations, while helping customers make informed choices about quality, safety, and scale. The role also develops the expertise of field engineering colleagues and brings customer feedback to product and engineering teams.

Responsibilities

- Advise customer and account teams on complex GenAI use cases, including retrieval-augmented generation, agent systems, fine-tuning, and evaluation. - Scope architectures, identify technical risks, and build proofs of concept and reference implementations for large-scale LLM and deep-learning workloads. - Lead model customization engagements, including evaluation approaches suited to specialized domains where quality and safety matter. - Connect customer and field feedback with product and engineering teams, and communicate roadmap information back to field teams. - Mentor colleagues through workshops, reference architectures, and internal technical enablement. - Share technical expertise externally through briefings, talks, writing, or other public contributions.

Requirements

- Deep knowledge of current proprietary and open-source LLMs, their capabilities and trade-offs, and how to select models for different use cases. - Expertise in GenAI application patterns, including RAG, agents, tool use, fine-tuning, prompt and evaluation engineering, and safeguards. - Strong machine-learning and deep-learning foundations, including distributed training, GPU workloads, and the MLOps lifecycle. - Proficiency in Python and relevant ML tools such as PyTorch, Transformers, MLflow, and Spark; able to build production-quality implementations. - Experience with a modern data and AI platform, or ability to ramp up quickly, plus cloud experience on AWS, Azure, or GCP. - Strong consulting, communication, and technical leadership skills; able to work credibly with engineers and senior technical decision-makers. - Based in the UK, able to work across EMEA time zones, and willing to travel to customer sites within the region.

Nice to have

- Experience helping digital-native customers build data or machine-learning solutions. - Public technical contributions such as talks, blogs, or open-source work in ML or GenAI. - Experience in regulated or high-stakes sectors, or partnering with product and engineering teams to influence a roadmap.

Benefits and work setup

- Remote or hybrid work in Germany is indicated, with travel across Europe required. The role description also specifies a UK base and EMEA time-zone coverage.