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# Platform Engineer

- **Company:** Ai.
- **Location:** UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Distributed Systems, Python (Programming Language), Machine Learning, System Programming, Systems Integration, Google Cloud, Cloud Platform System, Cloudformation, Machine Learning Operations, Terraform, Devsecops, Docker
- **Published:** October 14, 2026
- **Apply:** https://startup.jobs/platform-engineer-faculty-8367639

## About the Role

* You are an engineer who is passionate about building internal tools and takes pride in creating the foundational systems that enable others to excel.
 * You being experience and knowledge of at least one of the major cloud providers (AWS, GCP or Azure)
 * You possess modern systems programming skills in Python or Go, and you are comfortable selecting the best-fit technology for complex infrastructure challenges.
 * You bring practical experience with Docker containerisation and orchestration, to manage distributed systems at scale.
 * You have a strong background in Infrastructure-as-Code (IaaC) using tools like Terraform or CloudFormation, combined with a deep interest in DevSecOps practices.
 * You thrive in small, ambitious teams where you can take high levels of ownership and communicate effectively with both technical and non-technical peers.

## Description

As an engineer in the AI Platform team, you will be the architect of the infrastructure that makes world-class AI possible. Working closely with the Applied AI team, you'll be building and maintaining the data science, MLOps, and deployment tooling that empowers our team of over 100 Data Scientists and Engineers.

You will take ownership of the platform that enables us to transition from complex exploration to full-stack, production-grade machine learning products, ensuring our solutions are high-performing, scalable, and seamlessly integrated into diverse client environments.

What you'll be doing:

 * Taking ownership of our existing deployment and MLOps tooling to ensure our software delivery remains a significant lever for quality and reliability.
 * Contributing to the continuous evolution of our technology stack, from building new features in our notebook development environments to refining model monitoring systems.
 * Collaborating with a small, fast-moving team of customer-facing technologists to design and build the infrastructure our delivery teams need to succeed.
 * Designing and implementing infrastructure-as-code and DevSecOps processes to support distributed, containerised microservices architectures.
 * Integrating our core platform services across multiple cloud environments, including AWS, Azure, and GCP, to provide flexible solutions for our global clients.
 * Scaling our internal enablement capabilities, acting as an entrepreneurial force that removes technical friction and accelerates the deployment of machine learning., 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.