Platform Engineer

Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here . We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence. Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen. About the team Bringing medicine to patients is complex, expensive and high-risk. Faculty’s Life Science’s team is concentrated on building AI solutions which optimise the research and commercialisation of life-changing therapies. We partner with major pharma firms, academic research centres and MedTech start-ups to design and deliver solutions which address critical healthcare challenges, and help to democratise health for all. About the role We're looking for a Platform Engineer to build the backbone of applied artificial intelligence for our customers. You will design, build, and deploy robust, secure, and scalable cloud infrastructure that powers cutting-edge data and machine learning workflows. Working in a cross-functional team, you'll solve complex challenges and empower our data scientists and ML engineers to deploy their work effectively, shaping the future of AI solutions. What you'll be doing: Building robust, secure, and scalable cloud infrastructure for AI and ML workflows. Partnering with technical and non-technical stakeholders, from initial idea generation through to implementation and shipping. Enabling Machine Learning Engineers and Data Scientists by contributing to internal best practices, standards, and reusable code repos. Proactively identifying and recommending new ways customers can leverage cloud infrastructure to solve their key challenges. Creating and maintaining reusable, company-wide libraries and infrastructure-as-code. Researching and integrating the best open-source technologies to enhance Faculty's infrastructure capabilities. Who we're looking for: You are pragmatic and outcome-focused, balancing the big picture with the details to execute complex projects in the real world. You think scientifically, always testing assumptions, seeking evidence, and looking for opportunities to improve how things are done. You have a drive to learn, constantly exploring new technologies and novel applications for existing tools. You possess deep experience with both Azure and AWS as well as Infrastructure as Code, es