[All remote jobs](https://jobicy.com/jobs.md)[![Databricks logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/12/4e3f864ba9cf3c62d471e1c62414d098.jpg)](https://jobicy.com/company/databricks.md)[Databricks](https://jobicy.com/company/databricks.md) # Lead Solutions Architect – Generative AI (EMEA Emerging DNB) AI Summary Databricks seeks a senior customer-facing solutions architect to lead Generative AI engagements for emerging digital-native customers across EMEA. The role combines hands-on architecture and proof-of-concept delivery for RAG, agents, LLM fine-tuning, evaluation, MLOps, and GPU-scale workloads on the Databricks platform. The architect will partner with account teams, influence product roadmaps, mentor field engineers, and act as an external technical thought leader. Success requires deep Python and ML expertise, strong consultative communication, and the ability to move quickly with high-growth customers. The role is UK-based and supports customers across EMEA time zones.[Apply for this job](#job-application)[View company](https://jobicy.com/company/databricks.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished9 Nov 2026Apply before9 Dec 2026Listing views34Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. 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](https://jobicy.com/company/databricks.md) (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 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. * Based in the United Kingdom and able to work across EMEA time zones, with willingness to travel to customer sites within the region as needed. * Background helping digital-native customers build data and ML solutions (e.g., prior experience at a data/ML platform or consulting vendor) is a strong plus. ### Nice to Have * Public technical presence: conference speaking, published talks, blogs, or open-source contributions in the ML/GenAI space. * Domain depth in a regulated or high-stakes industry (healthcare/life sciences, financial services) where model quality, evaluation, and safety are critical. * Experience partnering with Product and Engineering teams to influence roadmap. ### Why This Role You will work on the hardest and most exciting GenAI problems our EMEA Emerging DNB customers have — companies building the future in real time — with the platform and the people to actually solve them, not just advise. You’ll shape how one of EMEA’s highest-growth business units adopts Generative AI, influence the direction of the product, and build a public profile as a recognised expert in the field. This is the role for a builder and a teacher, who wants their expertise to have outsized impact. About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on [LinkedIn](https://www.linkedin.com/company/databricks), [X](https://x.com/databricks), [YouTube](https://www.youtube.com/@Databricks), and [Instagram](https://www.instagram.com/databricksinc/?hl=en). Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click [here](https://docs.google.com/document/d/154un3e8Xav4BceOSlcYFZRGEuQI54xMxVydRwQn54eQ/edit?usp=sharing). Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer’s discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone. Show more [Apply now >](https://jobicy.com/jobs/159901-lead-solutions-architect-generative-ai-emea-emerging-dnb-2.md) > Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightЭто высокоуровневая роль, требующая одновременной экспертизы в production GenAI, распределённом ML, облаках и архитектуре клиентских решений. Сложность повышают стратегические заказчики, быстрые циклы итераций и необходимость влиять на технические решения без прямого управленческого контроля. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$205,000US market range (per year)$170k–$245k0$269.50k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. Работодатель не раскрыл компенсацию. Указана ориентировочная годовая оценка в USD для ведущего специалиста по GenAI-архитектуре; период year выбран как логичный для штатной senior/lead-роли, а не как явно заявленный работодателем. ## Core skills Skills and capabilities most closely associated with this opportunity. [Generative AI](https://jobicy.com/jobs?search_keywords=Generative%20AI.md)[Large Language Models](https://jobicy.com/jobs?search_keywords=Large%20Language%20Models.md)[Retrieval-Augmented Generation](https://jobicy.com/jobs?search_keywords=Retrieval-Augmented%20Generation.md)[LLM Fine-Tuning](https://jobicy.com/jobs?search_keywords=LLM%20Fine-Tuning.md)[Prompt Engineering](https://jobicy.com/jobs?search_keywords=Prompt%20Engineering.md)[LLM Evaluation](https://jobicy.com/jobs?search_keywords=LLM%20Evaluation.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[Transformers](https://jobicy.com/jobs?search_keywords=Transformers.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md)[Apache Spark](https://jobicy.com/jobs?search_keywords=Apache%20Spark.md)[Cloud Architecture](https://jobicy.com/jobs?search_keywords=Cloud%20Architecture.md)[GPU Workloads](https://jobicy.com/jobs?search_keywords=GPU%20Workloads.md)[Databricks](https://jobicy.com/jobs?search_keywords=Databricks.md) Sample interview questionsHow would you design a production RAG solution for a customer with strict data-governance requirements?I would begin with data classification, access controls, and quality requirements, then design ingestion, chunking, embedding, retrieval, and response-generation pipelines. I would enforce permissions at retrieval time, version datasets and prompts, implement evaluation datasets and monitoring, and use guardrails to reduce unsafe or unsupported responses. How do you decide whether to use prompting, RAG, fine-tuning, or an agentic workflow? I assess the source of required knowledge, task repeatability, latency and cost constraints, risk profile, and desired reliability. RAG is usually best for grounded, changing enterprise knowledge; fine-tuning is appropriate for durable behavioral or domain adaptations; prompting can solve simpler tasks; and agents are justified only when controlled multi-step tool use creates clear value. Describe how you would evaluate an LLM application before production deployment. I would define task-specific success criteria, build representative offline test sets, and measure quality, groundedness, safety, latency, and cost. I would combine automated and human evaluation, including judge-model approaches where validated, then establish production monitoring and a feedback loop for regression testing. How would you explain LLM trade-offs to both engineers and senior executives? For engineers, I would discuss model capabilities, architecture choices, failure modes, observability, and implementation constraints. For executives, I would frame the same choices around business outcomes, risk, time to value, operating cost, and measurable success metrics, while making assumptions and limitations explicit. What approach would you take to mentoring solutions architects and engineers in GenAI? I would combine reusable reference architectures, hands-on workshops, paired customer engagements, and practical evaluation templates. I would encourage teams to share lessons from deployments, document repeatable patterns, and build technical confidence through iterative practice rather than one-time training. 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