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# Machine Learning Engineer

- **Company:** Ai.
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Python (Programming Language), Machine Learning, Pair Programming, Tensorflow, Software Construction, Workflow Management Systems, Google Cloud, Pytorch, Scikit Learn, Machine Learning Operations, Docker
- **Published:** October 4, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/machine-learning-engineer/50346681

## About the Role

you'll be doing: 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. 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 Our Interview Process Talent Team Screen (30 minutes) Pair Programming Interview (90 minutes) System Design Interview (90 minutes) Commercial Interview (60 minutes) Our Recruitment Ethos We aim to grow the best team - not the most similar one. We know that diversity of individuals foster diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We're united by a deep intellectual curiosity and desire
to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations. If you don't feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours. A note on AI: we're happy for you to use it for research and interview prep, but please don't use it to generate answers during live interviews. We also use an AI note-taker (Metaview) in interviews so interviewers can stay present (which you can opt out of just let us know,) and every application is reviewed by a human, never decided by AI. #J-18808-Ljbffr

## Description

pioneering meaningful change in the clean energy revolution. Our vision is to accelerate the transition to net-zero emissions and drive efficiencies for a new era of utility companies. We believe that the responsible, and intelligent, deployment of AI is critical to the success of this mission. We partner with a wide range of clients - from major energy operators, to GreenTech startups, and national infrastructure providers - to build solutions which return measurable impact and move us towards a smarter, cleaner, and more sustainable world. About the role Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. 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. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems. What