Senior ML Engineer (Databricks) in London

Who we are: Kubrick is a next-generation technology consultancy, designed to accelerate delivery and build amazing teams. We deliver services across data, AI, and cloud and we’re building the next generation of tech leaders. Since 2017, we have established a market leading position supporting our clients build their data and technology teams and deliver enduring solutions.

The Role: We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our growing community specialising in Databricks. The successful applicant will have a strong background in training models to support a range of problem domains and be well versed in delivering and maintaining models in a production environment through applying MLOps best practice. The role will require familiarity with the relevant capabilities of Databricks and at least one of the major cloud service providers (AWS, Azure, or GCP). Advanced proficiency in Python and SQL is essential and an academic background in a related discipline is preferred.

As a Senior ML Engineer in our Kubrick Advanced team, you will play a key role in delivering high quality AI/ML and data engineering projects to our clients, with Databricks serving as the primary platform for solution development. You will work closely with Databricks’ professional services teams and client stakeholders to design and implement Lakehouse aligned architectures, leveraging Delta Lake, Unity Catalog, MLflow, and Databricks Model Serving as part of robust end to end solutions. Alongside hands on development, you will frequently take on leadership responsibilities within Kubrick delivery squads, providing technical guidance, enforcing best practices, and ensuring solutions are scalable, secure, and aligned with Databricks standards throughout the project lifecycle. You will also contribute to the ongoing growth and capability development of Kubrick, in strengthening our Databricks delivery proposition. This will include supporting the development of internal accelerators, championing best practice use of the Lakehouse Platform, and assuming line management or technical leadership responsibilities within the team.

Key Responsibilities

• Lead technical delivery within Kubrick’s squads deployed on client project engagements, ensuring our solutions follow Databricks Lakehouse best practices and that Kubrick is recognised for the quality, scalability, and robustness of the technical solutions we provide.
• Work with Kubrick & client staff of other disciplines to understand and assess requirements, design Lakehouse aligned architectures, and inform delivery planning that leverages Databricks capabilities such as Delta Lake, Unity Catalog, MLflow, and Databricks Workflows.
• Seek, build, and maintain effective client relationships contributing to Kubrick’s commercial priorities while strengthening our collaborative partnership model, particularly in data & AI engagements delivered on Databricks.
• Line managing developers within the team, supporting their technical development with a focus on Databricks engineering best practices, certified learning paths, and production grade ML delivery standards.
• Promote a culture of engineering excellence within KA through curiosity, collaboration, and contributions to our internal Databricks knowledge base, accelerators, and delivery playbooks.
• Actively participate in continuous learning and upskilling, including pursuing Kubrick funded Databricks certifications and engaging in self directed or group learning to ensure your technical capabilities remain modern and industry relevant.

Required Skills & Experience

• Experience in Machine Learning and/or Data Science, including building, deploying, and operating production grade ML model, ideally within a Lakehouse architecture.
• Hands‑on practical experience training, finetuning, and deploying ML models on Databricks, including use of MLflow for tracking and model registry, Model Serving, and Delta Lake as the underlying data layer. Holding a Databricks ML Engineer certification is highly desirable.
• Strong ability to “pick the right tool for the job,” selecting appropriate modelling approaches, frameworks, and Databricks native capabilities to address a given problem statement.
• Awareness of the cost implications of training, finetuning, testing, and serving ML models on Databricks, including cluster configuration, autoscaling, and job orchestration.
• Deep AI/ML subject matter expertise, combined with the communication skills needed to explain technical concepts clearly and influence both technical and business stakeholders.
• Demonstrable experience in delivery leadership and/or line management, including mentoring junior technical personnel—ideally within a Databricks-centric engineering environment.

Development Opportunities

• 20 dedicated development days. Four of these will be quarterly collective training days and the remainder will be informed by your own professional development plan.
• Support for Professional accreditations in our partner technologies, e.g. Databricks, Azure, AWS etc.
• Close collaboration opportunities with principal consultants and senior members of the business.