At a Glance
- Tasks: Lead the design and delivery of a cutting-edge MLOps platform on AWS.
- Company: Join CreateFuture, a top digital consultancy with a people-first culture.
- Benefits: Enjoy flexible working, competitive salary, and opportunities for personal growth.
- Other info: Be part of a diverse team that values inclusion and collaboration.
- Why this job: Make a real impact by building innovative ML solutions for major brands.
- Qualifications: Expertise in AWS, SageMaker, and MLOps patterns required.
The predicted salary is between 63000 - 77000 £ per year.
Who we are
Create Future 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 Pay Pal, adidas, Natwest, Fan Duel 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 Create Future 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.
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 Sage Maker (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 Sage Maker, owning parity testing methodology and sign-off
- Build and maintain CI/CD pipelines (Code Pipeline/Code Build or equivalent) for automated model promotion across environments
- Define and enforce IAM least-privilege policies, KMS key management, and VPC/Private Link 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 & Sage Maker (must have)
- Amazon Sage Maker (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 (Code Pipeline / Code Build or equivalent) - advanced; you've automated model promotion across environments
- General and technical Python / Py Spark - 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 Cloud Watch / Cloud Trail for platform observability and audit
- AWS Glue / EMR for data processing pipelines
- AWS Lake Formation and Sage Maker Feature Store
- Amazon VPC / Private Link for secure ML endpoint networking
- Data governance & compliance experience (PII / GDPR)
- Infrastructure as Code (Terraform / Cloud Formation / CDK)
- What we'll offer you
- 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 Create Future offices when needed
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'll 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.
Inclusion at Create Future
We believe diverse teams build better workplaces and better products.
We want Create Future 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.
Our hiring process
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.
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Lead ML Ops Developer employer: Doist
Brink's is an exceptional employer that fosters a collaborative and innovative work culture, empowering employees to drive global commercial strategies in the ATM lifecycle solutions sector. With a strong focus on professional development and growth opportunities, employees are encouraged to expand their skills while contributing to sustainable growth and operational excellence. Located in a dynamic environment, Brink's offers unique advantages such as a diverse team and the chance to build long-term partnerships with global customers, making it a rewarding place to advance your career.
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We think this is how you could land Lead ML Ops Developer
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We think you need these skills to ace Lead ML Ops Developer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Doist. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
