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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead ML Ops Developer - **Company:** CreateFuture - **Location:** London, UK (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Amazon S3, Continuous Integration, Data Governance, Identity and Access Management, Python (Programming Language), Key Management, Machine Learning, Management of Software Versions, Delivery Pipeline, Amazon Virtual Private Cloud (VPC), Git, Cloudformation, Pyspark, Git Flow, Production Code, AWS Glue, Machine Learning Operations, Cloudwatch, Terraform, Software Version Control, Data Pipelines - **Published:** July 11, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=ecab8b1bfbf12b88 ## About the Role AWS & SageMaker (must have) * Amazon SageMaker (Studio, Training, Pipelines, Endpoints) - expert level; you can architect and operate the full lifecycle * AWS IAM - advanced; writes least-privilege policies from scratch, not just modifies examples * Amazon S3 - advanced; including lifecycle policies, encryption, and bucket policies * AWS KMS - working knowledge of key management in an ML context * CI/CD tooling (CodePipeline / CodeBuild or equivalent) - advanced; you've automated model promotion across environments General and technical * Python / PySpark - expert; production-quality code, not just notebook scripts * Statistical / parity testing methodology - advanced; you can design and execute parity sign-off on migrated models * MLOps pattern design (model registries, monitoring, governance gates) - expert; you've built and owned these patterns in production * Git / version control - advanced; branching strategies, PR workflows, and release tagging for ML artifacts * Track record of technical ownership - accountable for platforms that other teams depend on, not just your own workstream * Enablement mindset - you build patterns and hand them off so teams self-serve, rather than becoming a single point of failure * Risk communication - able to explain parity gaps, governance trade-offs, and migration risk to non-technical audiences * Decision-making under ambiguity - comfortable setting the technical pattern before all edge cases are known and iterating as issues emerge Nice to have: * AWS Step Functions / Lambda for workflow orchestration * Amazon CloudWatch / CloudTrail for platform observability and audit * AWS Glue / EMR for data processing pipelines * AWS Lake Formation and SageMaker Feature Store * Amazon VPC / PrivateLink for secure ML endpoint networking * Data governance & compliance experience (PII / GDPR) * Infrastructure as Code (Terraform / CloudFormation / CDK), Depending on the role, we might also ask you to do a short presentation, a practical or technical task or have a values focused conversation. We will explain what is involved before anything happens. ## Description We are looking for a Lead MLOps Developer to own the design and delivery of a production-grade machine learning platform on AWS., * Design and maintain a production MLOps platform on Amazon SageMaker (Studio, Training, Pipelines, Endpoints) - including model registry, automated retraining, drift monitoring, and governance gates * Lead the migration of a 12-model production suite (e.g., the CVM suite) from legacy infrastructure to SageMaker, owning parity testing methodology and sign-off * Build and maintain CI/CD pipelines (CodePipeline/CodeBuild or equivalent) for automated model promotion across environments * Define and enforce IAM least-privilege policies, KMS key management, and VPC/PrivateLink network controls for all ML workloads * Create the 'golden template' MLOps patterns - model packaging, versioning, monitoring, and compliance gates - that other teams self-serve from * Produce technical documentation and runbooks that enable data science teams to operate pipelines without central bottlenecks * Communicate parity gaps, governance trade-offs, and migration risk clearly to non-technical stakeholders and project sponsors * Size and sequence interdependent migration work, making sound technical decisions before all edge cases are known and adapting as issues surface, We trust people to do their best work. That means flexibility over rigid rules, impact over activity, and real investment in your growth both professionally and personally. You'll be part of a supportive, and friendly culture, surrounded by smart, curious people who care deeply about what they do. We offer flexible working, including hybrid and remote options. Our office hubs are located in Edinburgh, Leeds, Manchester, London and Bulgaria, with occasional travel to client sites or CreateFuture offices when needed. We trust you to manage your time balancing collaboration with client time and focused work. What matters is the impact you have, not how busy you look. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)