At a Glance
- Tasks: Design and implement cutting-edge ML and AI applications on the Databricks platform.
- Company: Join a leading tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on lifelong learning and community growth.
- Why this job: Be a thought leader in AI while making a real impact on enterprise solutions.
- Qualifications: 10+ years in data science/ML with strong customer-facing experience.
The predicted salary is between 70000 - 90000 £ per year.
As a Senior Specialist Solutions Architect (ML & AI), you will serve as the trusted technical ML and AI expert for Databricks customers and the Field Engineering organization. You will partner with Solution Architects to guide enterprise and strategic customers in architecting production-grade ML and AI applications on the Databricks Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting-edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI thought leader.
Impact you will have:
- Architecting Workloads: Design and implement production-level ML and AI workloads, including end-to-end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud-native services.
- GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool-calling, multi-agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Provide advanced technical support to Solution Architects during the technical sales cycle by building MVPs, leading deep-dive sessions, and aligning AI solutions with complex customer business challenges.
- Product Influence: Collaborate cross-functionally with product and engineering teams to represent the voice of the customer, define priorities, and influence the platform’s AI roadmap.
- Thought Leadership: Drive community growth and AI platform adoption through the creation of technical tutorials and training materials, as well as by presenting at industry conferences and leading hackathons.
What we look for:
- Experience: 10+ years of hands-on industry DS/ML experience, with a focus on either:
- ML Engineering: Building/maintaining production-grade cloud infrastructure (AWS/Azure/GCP) that supports deployment of ML applications and monitoring ML model performance.
- Data Science/AI: Applying advanced techniques in LLMs, agentic systems, vector databases, fine-tuning, and deployment tools (e.g., HuggingFace, Langchain).
- Hands-on experience working with Distributed Spark based systems.
- Experience with data engineering concepts or a good understanding of data engineering concepts.
- Pre-sales or post-sales experience working with external clients across a variety of industry markets. Minimum of 5+ years of customer-facing experience would be preferred.
- [Preferred] Experience working with Apache Spark™ to process large-scale distributed datasets.
- Communication: Proven ability to communicate and teach complex technical concepts to both technical and non-technical audiences.
- Core Traits: Passion for lifelong learning, collaboration, and driving business value through AI.
- Education: Graduate degree in a quantitative discipline (e.g., Computer Science, Engineering, Statistics, Operations Research, etc) or equivalent practical experience.
- Can meet expectations for technical training and role-specific outcomes within 3 months of hire.
- Can travel up to 30% when needed.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Specialist Solutions Architect (AI/ML) in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Databricks!
✨Show Off Your Projects
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Databricks.
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We think you need these skills to ace Senior Specialist Solutions Architect (AI/ML) in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Databricks, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Databricks. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Databricks
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Databricks!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
