About the Role
As a Senior Machine Learning Engineer, you'll lead development and deployment of cutting-edge AI systems for diverse clients. You'll design, build, and deploy scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards. This is an ambitious, cross-functional role requiring a blend of technical expertise, engineering leadership, and confident client-facing skills, within Faculty's Energy, Transition and Environment business unit, which focuses on accelerating the transition to net-zero emissions and driving efficiencies for utility companies, partnering with major energy operators, GreenTech startups, and national infrastructure providers.
Responsibilities
•Leading technical scoping and architectural decisions for high-impact ML systems
•Designing and building production-grade ML software, tools, and scalable infrastructure
•Defining and implementing best practices and standards for deploying machine learning at scale across the business
•Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities
•Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies
•Mentoring and developing junior engineers, actively shaping the team's engineering culture and technical depth
Who We're Looking For
•Understanding of the full ML lifecycle and significant experience operationalising models built with frameworks like TensorFlow or PyTorch
•Deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems
•Demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure
•Extensive experience working with container and orchestration tools such as Docker & Kubernetes to build and manage applications at scale
•Thrives in fast-paced, high-growth environments, demonstrating ownership and autonomy in driving projects to completion
•Communicates exceptionally well, confidently guiding both technical teams and senior, non-technical stakeholders
Interview Process
1.Talent Team Screen (30 minutes)
2.Pair Programming Interview (90 minutes)
3.System Design Interview (90 minutes)
4.Commercial Interview (60 minutes)
Open to conversations about part-time hours. Every application is reviewed by a human, never decided by AI.
About Faculty
Faculty was established in 2014 on the belief that AI would be the most important technology of our time. Since then, the company has worked with over 350 global customers to transform their performance through human-centric AI, bringing technical, product and delivery expertise to clients spanning government, finance, retail, energy, life sciences and defence.
