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Forward Deployed Engineer

Faculty· Engineering
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Hybrid (London)Full-timePosted 2mo ago

About the role

Machine Learning Engineer role delivering bespoke AI solutions for national security and safety-focused clients, translating complex ML concepts into production-ready systems and defining standard practices for deploying ML at scale.

Responsibilities

  • Building and deploying production-grade ML software, tools, and infrastructure.
  • Creating reusable, scalable solutions that accelerate the delivery of ML systems.
  • Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges.
  • Leading technical scoping and architectural decisions to ensure project feasibility and impact.
  • Defining and implementing Faculty’s standards for deploying machine learning at scale.
  • Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.

Qualifications

  • You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
  • You possess strong Python skills and solid experience in software engineering best practices.
  • You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.
  • You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale
  • You are comfortable with core ML concepts, including probability, statistics, and common learning techniques.
  • You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders.
  • You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions

Skills

Required:ML lifecycle executionPythonCloud platformsContainers & orchestration
Last updated Oct 21, 2026. Interested? Apply on the company site.
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