[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) Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [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 Oct 2026Apply before8 Nov 2026Listing views37Application actions0Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Databricks is hiring a senior individual-contributor Solutions Architect to lead Generative AI engagements for emerging digital-native and startup customers across EMEA. The role combines customer-facing architecture, hands-on proof-of-concept development, LLM fine-tuning, RAG and agent design, evaluation, safety, and MLOps. The successful candidate will partner with account and field engineering teams, influence product roadmaps, and mentor a broader SA/SE organization. It requires deep Python and ML-platform expertise plus strong executive communication and technical thought leadership. The position is remote/hybrid for candidates based in the United Kingdom, with regional customer travel as needed. ## 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 insightThis is a lead-level role requiring deep, current expertise across production GenAI systems, distributed ML, LLM customization, governance, and customer architecture. The candidate must independently solve ambiguous, high-stakes technical problems while earning credibility with both engineers and senior executives in fast-moving accounts. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$210,000US market range$170k–$270k0$297k AI insightNo actual compensation is disclosed, so these figures are estimates rather than offer terms. For the US market, a lead-level, customer-facing Generative AI Solutions Architect with advanced LLM, MLOps, cloud, and technical leadership responsibilities would commonly command estimated annual base compensation around $170,000-$270,000 USD, with an estimated midpoint of $210,000; equity and variable compensation may increase total pay. ## 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)[Solutions Architecture](https://jobicy.com/jobs?search_keywords=Solutions%20Architecture.md)[Retrieval-Augmented Generation](https://jobicy.com/jobs?search_keywords=Retrieval-Augmented%20Generation.md)[Agentic Systems](https://jobicy.com/jobs?search_keywords=Agentic%20Systems.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Deep Learning](https://jobicy.com/jobs?search_keywords=Deep%20Learning.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md)[Cloud Architecture](https://jobicy.com/jobs?search_keywords=Cloud%20Architecture.md) Sample interview questionsHow would you design a production RAG system for a customer handling sensitive, regulated information?I would begin with clear use cases, risk classification, and measurable quality targets. The architecture would use governed ingestion, document-level access controls, metadata filtering, a vector index, retrieval observability, and a model-serving layer with prompt-injection defenses and output guardrails. I would establish an offline evaluation set plus ongoing production monitoring for retrieval relevance, groundedness, safety, latency, and cost. A customer wants to fine-tune an open LLM. How would you determine whether fine-tuning is preferable to prompting or RAG? I would first define the failure mode and success metrics. RAG is usually best for current, proprietary, or frequently changing knowledge, while prompting can address many formatting and workflow needs quickly. Fine-tuning is appropriate when the model needs durable behavioral adaptation, domain style, structured outputs, or task performance that cannot be achieved reliably through prompt engineering and retrieval; I would validate this with controlled evaluations and cost analysis. How would you evaluate an agentic workflow before production deployment? I would create a representative test suite covering successful paths, edge cases, unsafe requests, tool failures, and adversarial inputs. Evaluation would combine deterministic checks, human review for high-risk scenarios, and model- or judge-based scoring calibrated against human labels. I would also measure tool-call accuracy, task completion, hallucination or unsupported-action rates, latency, cost, and recovery behavior. Describe how you would communicate a complex GenAI architecture trade-off to both engineers and an executive sponsor. For engineers, I would provide architecture diagrams, interface definitions, benchmark results, evaluation methodology, and explicit failure-mode analysis. For executives, I would frame the same decision around business outcomes, delivery timeline, risk, investment, and measurable success criteria. In both cases, I would make the recommendation clear, state assumptions, and identify decisions that require stakeholder input. How would you enable other Solutions Architects and Solutions Engineers to deliver GenAI engagements more effectively? I would build reusable reference architectures, starter repositories, evaluation templates, discovery guides, and technical workshops organized around common customer patterns such as RAG, agents, fine-tuning, and governance. I would pair with teams on early strategic engagements, capture lessons learned, and turn them into repeatable playbooks. Success would be measured through adoption of these assets, improved technical win rates, and the field team's ability to independently deliver high-quality GenAI solutions. 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 (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/154888-lead-solutions-architect-generative-ai-emea-emerging-dnb.md) > Annual salary information is not provided for this position. 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