Within Leonardo UK, our Cyber & Security Division leads the charge in digital transformation, delivering sophisticated solutions that protect both civil and defence infrastructure around the world. This role sits at the heart of our Data Practice, a growing team dedicated to unlocking the power of data across high-stakes domains like Defence, Telecommunications, Energy, and Finance.
Step into a high-impact, hands-on role where you'll be shaping the future of secure data systems and AI capabilities in some of the most critical areas of the UK’s national infrastructure. As a Machine Learning Engineer, you’ll be part of a forward-thinking team solving complex, real-world challenges through advanced analytics, automation, and intelligent systems.
This is your chance to work on projects that blend traditional enterprise IT with bespoke Operational Technology, all in a fast-paced, collaborative environment. We're looking for curious, creative minds—people who are not only technically skilled in Python and machine learning but also eager to explore, learn, and build secure, scalable AI solutions.
What you’ll be doing as a Machine Learning Engineer:
- Building, integrating, testing and scaling models including NLP and Computer Vision.
- Take ownership of developing, training and productionising machine learning lifecycles, adhering to best practices, security needs and quality assurance.
- Developing deep learning architectures and implementing neural networks tailored to complex use cases.
- Implementing and maintaining MLops workflows
- Collaborating closely with Data Engineers and DevOps teams to support continuous integration, deployment, and automation.
- Exploring new tools and frameworks to keep your solutions modern and efficient.
- Working with cloud-native platforms, Linux/Windows environments, and big data technologies like Apache Spark.
- Engaging directly with stakeholders to align model development with broader product and business goals.
What you’ll bring
- UK SC Clearance or the ability obtain it.
- Awareness of deep learning model architecture and when is appropriate to use them.
- Strong understanding of how machine learning components integrate within the broader system architecture and contribute to overall project objectives.
- Understanding of how to use APIs to deploy machine learning models.
- Hands-on experience with containerization.
- Strong grasp of machine learning frameworks (e.g. PyTorch, Tensorflow).
- Knowledge of machine learning architectures, loss functions, tools and techniques.
- Experience developing & training machine learning models, including hyperparameter tuning and optimizing model performance.
- Experience with (or at least exposure to) MLOps workflows, such as CI/CD pipelines.
- Experience with Python and SQL.
- Critical thinking and ability to problem solve.
- Experience in developing data pipelines, including exposure to ETL (Extract, Transform, Load) processes
- Good understanding of software engineering principles such as OOP and TDD.
- Strong experience with best practices such as version control and unit testing.
- Ability to communicate technical concept to non-technical colleagues.
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