Kraken

Senior Machine Learning Engineer – Utilities

Kraken

full-time

Posted on:

Location Type: Hybrid

Location: London • United Kingdom

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Job Level

Senior

Tech Stack

AWSCloudKubernetesNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

  • Design, build and deploy machine-learning and AI-powered systems that solve real business and customer problems
  • Work end-to-end: from data exploration and experimentation through to production deployment, monitoring and iteration
  • Collaborate closely with product managers and engineers to shape solutions that are practical, scalable and maintainable
  • Lead deep technical investigations into complex or ambiguous problems, including critical bugs across multiple systems
  • Help define and improve ML and engineering best practices within the team
  • Run and analyse experiments (e.g. A/B tests) to validate product and model improvements
  • Stay up to date with advances in ML, GenAI and developer tooling, and apply them thoughtfully to our products
  • Contribute to a culture of learning through knowledge sharing, internal talks and mentoring

Requirements

  • Strong hands-on experience applying machine learning in production environments (industry or equivalent research experience) with a proven track record of writing maintainable, testable code in complex codebases.
  • Excellent Python skills and solid SQL experience
  • Deep understanding of ML fundamentals: data analysis, model selection, evaluation, deployment and monitoring
  • Experience working with ML / data libraries such as pandas, NumPy, scikit-learn, PyTorch or TensorFlow
  • Comfort working in a software-engineering-heavy environment (version control, CI/CD, code reviews, MLOps principles)
  • Experience building and operating systems on cloud infrastructure (AWS preferred)
  • Ability to clearly explain technical concepts and trade-offs to a wide range of stakeholders
  • Confidence working autonomously, asking questions early, and collaborating across teams and with clients
  • Nice-to-have: Experience building GenAI or NLP-based products
  • Nice-to-have: Exposure to LLM tooling, prompting, agents or evaluation techniques
  • Nice-to-have: Experience with Kubernetes, dbt, or modern data tooling
  • Nice-to-have: Experience running production experiments (A/B testing)
  • Nice-to-have: Experience mentoring junior colleagues and leading workstreams
Benefits
  • Flexible working arrangements
  • Professional development opportunities
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
machine learningPythonSQLdata analysismodel selectionmodel evaluationmodel deploymentmodel monitoringA/B testingGenAI
Soft Skills
collaborationcommunicationautonomyproblem-solvingmentoring