Lead ML Ops Developer

Who We Are CreateFuture is fast becoming the UK’s most recognisable digital consultancy, with years of experience building digital products and services for major organisations whilst putting our people first. We have offices in the centre of Edinburgh, Leeds, Manchester, and London as well as remote employees located throughout the country.

We are a team of creators - whether that’s code, project plans, go to market strategies, culture initiatives, marketing campaigns, large language models or people policies. And together, with our clients, we create the future. This has seen us collaborate and partner across a multitude of industries and sectors, with the likes of PayPal, adidas, Natwest, FanDuel and Money Saving Expert, to name just a few.

Our reputation as a partner determined to deliver high-quality, robust and thoughtful products has enabled us to scale to over 500 people in the last couple of years, and it is our amazing people - along with the safe, supportive and friendly culture we have built - that makes CreateFuture a great place to work. Don’t just take our word for it though, we have been recognised by Best Workplaces UK multiple years in a row - across a number of categories - and our employee exit rate is astonishingly low.

Join us on our journey… Let’s create something awesome, together, today.

About The Role And Team We are looking for a Lead MLOps Developer to own the design and delivery of a production-grade machine learning platform on AWS.

What you’ll be doing

• 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

What we’re looking for

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 …