• 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.
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Who We're Looking For

• 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