Head of Engineering

Help us use technology to make a big green dent in the universe!

Kraken powers some of the most innovative global developments in energy.

We’re a technology company focused on creating a smart, sustainable energy system. From optimising renewable generation, creating a more intelligent grid and enabling utilities to provide excellent customer experiences, our operating system for energy is transforming the industry around the world in a way that benefits everyone.

It’s a really exciting time in energy. Help us make a real impact on shaping a better, more sustainable future.

Our tech platform ‘Kraken’ is already licensed to support 55 million customer accounts globally, and we aim to serve 100 million by 2027. Kraken is the most AI-driven, innovative, forward-thinking platform for energy management. From optimising resources to delivering cost-effective, exceptional customer experiences through advanced Customer Information Systems (CIS), billing, meter data management, CRM, and AI-driven communications.

We’re now charging the Kraken platform to other utility industries (Water and Broadband) and have created a new team called – Kraken Utilities. Over the last 3 years we have built this team from scratch to re-architect, design, and develop our Kraken software platform to solve complex industry wide problems within the water and broadband sectors (such as customer experience & water leak detection).

The Kraken Utilities team is in a very exciting growth phase, and has already signed six key clients:
Severn Trent, Leep, Portsmouth Water, Essential Energy, Talk Talk, and Cuckoo. We are currently 120+ people strong globally.

As Head of Engineering for Data, ML & AI Tools, your main responsibility will be to lead multiple high-impact teams building AI-powered tools, voice interfaces, analytics products, and machine learning models that support utility clients and internal teams. You'll manage a group of Engineering Leads and collaborate closely with cross-functional teams to define technical strategy, deliver production-grade systems, and drive innovation across LLM, data, and ML domains.

Your teams will be responsible for delivering agent-assist tools like Answer Bots, customer-facing Voice Bots, predictive models for utility use cases (e.g., water leakage detection, churn), and client dashboards that turn data into actionable insights.

• Lead and scale our cross-functional Engineering, ML and data engineering teams, including machine learning engineers, LLM/AI engineers, data analysts, and Software engineers.
• Drive the development of intelligent tools for customer agents (e.g., LLM-based Answer Bots) and end-users (e.g., Voice Bots) that improve service efficiency and experience.
• Guide the team delivering predictive ML models for utility clients in areas like water leak detection and churn prediction, ensuring robustness, explainability, and client value.
• Oversee the development of data analytics products – including dashboards and data pipelines – that deliver actionable intelligence to utility clients.
• Set the long‑term technical vision for our AI & analytics products while being hands‑on in system design, architecture reviews, and high‑impact technical decisions.
• Partner closely with cross-functional stakeholders, including product managers, customer teams, and delivery leads, to align technical solutions with business goals.
• Champion experimentation, prototyping, and fast iteration to explore new use cases with GenAI and classic ML across business domains.
• Build systems that combine structured data, unstructured customer interactions, and real‑time operational signals, powering the next generation of intelligent decision support.
• Stay on the forefront of emerging technologies in LLMs, RAG (Retrieval‑Augmented Generation), reinforcement learning, and agentic workflows — and translate them into practical, scalable products.

• Proven leadership experience in managing multi-disciplinary engineering teams (20+ engineers), including ML, LLM, data engineering, and platform engineering.
• Understanding of ML lifecycle – from data ingestion and feature engineering to model…