Senior Machine Learning Engineer – AI Foundations

🕒 January 27

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Kraken

WebsiteLinkedInAll Job Openings

201 - 500 employees

⚡ Energy

☁️ SaaS

🏢 Enterprise

Energy • SaaS • Enterprise

Kraken is an innovative technology platform designed for the utility sector, providing an end-to-end solution that automates and enhances the energy supply chain. It manages over 60 million customer accounts and works with various energy sources, including offshore wind and grid-scale batteries. Kraken helps utilities improve operational efficiency, customer service, and innovative product development while contributing to the transition to a decentralized and decarbonized energy system.

📋 Description

• Build and maintain the foundational AI gateways and inference services used across Kraken to provide reliable and efficient access to ML and generative AI models. • Architect and evolve internal evaluation tooling and monitoring frameworks that allow teams to measure the performance, quality, and safety of their systems at scale. • Act as a technical mentor by teaching software engineers and ML specialists how to adopt foundational capabilities, ensuring AI is easy to use and integrated into everyday development. • Create and maintain high-quality documentation, internal guidance, and technical standards to help teams understand when and how to use AI effectively. • Continuously improve Kraken's approach to AI enablement by balancing speed, cost, and quality within the infrastructure you manage.

🎯 Requirements

• ~3 years of professional experience as a Machine Learning Engineer or similar applied ML role. • Strong Python skills and experience with common ML libraries and frameworks. • Practical experience taking ML models from development into production. • Good understanding of software engineering fundamentals (version control, testing, CI/CD etc). • Experience working with cloud infrastructure and data pipelines. • An ability to explain ML concepts clearly to non-ML engineers. • A bias towards action, learning quickly, and improving systems over time. • Prior experience building internal platforms or shared tooling. • Exposure to MLOps practices, including model monitoring, evaluation, and deployment automation. • Familiarity with considerations regarding data privacy, security, or responsible AI.

🏖️ Benefits

• Health insurance • Paid time off • Flexible work arrangements • Professional development