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.
- 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
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)
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.
We try to keep our hiring process clear, fair and respectful of your time. We aim to get back to everyone who applies and we will be upfront about where you are in the process.
It usually looks like this:
- Call with our Talent Acquisition Team
- Role specific capability interview
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.
We believe diverse teams build better workplaces and better products. We want CreateFuture to be a place where people feel able to be themselves and do their best work.
If you need any adjustments or support during the application process, just. We will do what we can to help.
We look forward to your application!
