AI Engineering Manager

Description

The company is an energy business with a family of brands, around 21,000 colleagues, and a focus on building an energy system that does not depend on fossil fuels. It makes, stores, moves, sells and repairs energy.

You will lead building and shipping AI products that help the company get value from its data and newer technologies. You will guide a team of AI engineers through designing, building, deploying and running scalable AI and large language model (LLM) applications. You will work with data scientists, product teams and business stakeholders to make sure these products are secure, reliable and meet enterprise standards.

You will report to the Director of AI & Data Science and help shape the company's AI engineering function. You will set good practice in software engineering and MLOps and help move machine learning models and AI solutions from experiments into production. This role suits a strong technical leader who enjoys developing people and still stays close to architecture, solution design and hands-on engineering.

Responsibilities

• Give technical direction to the AI engineering team and set high standards for software engineering, MLOps and responsible AI.
• Lead the design, build and release of AI applications, including LLM-based solutions, and contribute code, architecture and technical decisions yourself.
• Help data scientists deploy, monitor and maintain models, and turn machine learning and AI work into production products.
• Work with data scientists, product owners, business stakeholders and data engineers to turn business needs into practical AI solutions that match business priorities.
• Build quality, security, reliability and continuous improvement into every stage of delivery across AI engineering.
• Mentor and grow AI engineers, fostering collaboration, learning, innovation and a growth mindset.
• Keep up with new AI, MLOps and engineering practices so the team stays current.

Requirements

• Significant experience in AI engineering, machine learning, software development or a related technical field, with a record of getting AI and machine learning solutions into production.
• Experience leading, mentoring and developing high-performing technical teams, building a culture of collaboration, innovation, continuous improvement and professional growth.
• Hands-on experience designing, building and deploying LLM-powered solutions and generative AI applications at enterprise scale.
• Strong Python skills and experience with machine learning frameworks such as PyTorch, TensorFlow and Scikit-learn.
• Deep knowledge of AI engineering, MLOps and modern LLM technology, including prompt engineering, Retrieval-Augmented Generation (RAG), AI agents and model orchestration frameworks.
• Experience running MLOps capabilities such as model lifecycle management, deployment automation, experiment tracking, monitoring, governance and machine learning pipelines.
• Proven work with cloud platforms and modern engineering practice, including Azure, Databricks, CI/CD pipelines, version control and DevOps methods.
• Ability to design, build, deploy and support scalable, secure, reliable, production-grade AI solutions.
• Strong technical leadership: shaping architecture, setting engineering standards, guiding technical decisions and driving delivery quality.
• Experience delivering complex technology programmes while balancing innovation with operational reliability and business priorities.
• Ability to mentor and coach engineers, build technical excellence and grow capability across the wider AI engineering team.
• Strong stakeholder management and communication, with the ability to engage and influence senior leaders, product owners, data scientists, engineers and business stakeholders.
• Experience turning business requirements and challenges into technical solutions that deliver measurable commercial value.
• Strong analytical thinking and problem solving, with the ability to handle ambiguity, manage competing priorities and make sound engineering decisions.
• Understanding of data governance, responsible AI principles and the regulatory considerations for AI solutions.
• Commercial awareness of how AI, data and emerging technologies can drive business results in a large, complex organisation.
• A degree in Computer Science, Engineering, Mathematics, Data Science or a related subject, or equivalent practical experience.
• Certifications in AI, machine learning, cloud technologies, data engineering or MLOps are an advantage.

Conditions

The company works hard to support the varied circumstances of its people and designs its total rewards so you can choose what you need, when you need it.

Benefits

• Total rewards that let you choose support for yourself and your family, covering finances, physical health and emotional wellbeing.
• Opportunities for growth and a team where your voice counts.