Machine Learning Engineer

Requirements

• Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC)

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• You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch

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• You possess strong Python skills and solid experience in software engineering best practices

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• You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security

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• You’ve worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale

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• You are comfortable with core ML concepts, including probability, statistics, and common learning techniques

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• You’re an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders

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• You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions

What the job involves

• Our Defence team is focused on building and embedding human-centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting-edge AI to high-stakes situations and maintain the balance of global powers essential to our liberty

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• You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices

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• Working with clients, and cross-functional teams, you’ll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems

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• Building and deploying production-grade ML software, tools, and infrastructure

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• Creating reusable, scalable solutions that accelerate the delivery of ML systems

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• Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges

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• Leading technical scoping and architectural decisions to ensure project feasibility and impact

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• Defining and implementing Faculty’s standards for deploying machine learning at scale

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• Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders

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