ML Ops Engineer at CreateFuture, Leeds/Remote, £Contract Rate

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.

Lead MLOps Engineer (Contractor)

Department

Cloud & Data Engineering

About the role and team

You will act as the Lead MLOps Engineer for a time-bound delivery programme focused on migrating production ML workloads from Databricks to AWS SageMaker within a regulated environment. This role owns the technical direction, delivery integrity, and coordination across all technical workstreams.

The immediate priority is a container-first migration of existing Databricks-hosted ML workloads to AWS, with SageMaker as the default execution platform and a hard commercial deadline. In parallel, you will help define the future MLOps operating model on SageMaker, which will become business-as-usual once the migration completes.

You will lead and coordinate work across multiple streams (standardised migrations, complex/edge-case workloads, platform foundations), working closely with Data Engineers, Cloud Engineers, Delivery Management, and Data Science SMEs. This is a hands-on technical leadership role: you will set patterns, review work, unblock delivery, and personally handle the most complex migrations.

What you’ll be doing

Technical leadership & delivery ownership

• Acting as the overall technical authority for the programme, owning architectural decisions, execution patterns, and technical quality across all workstreams.
  
  
• Defining and enforcing standard migration patterns for moving ML workloads from Databricks into AWS SageMaker, while managing exceptions for complex or legacy cases.
  
  
• Coordinating delivery across parallel teams, validating throughput assumptions, sequencing, and dependencies.
  
  
• Providing technical input into delivery planning, risk management, and milestone sign-off, working closely with delivery leadership.
  
  

MLOps & platform engineering (AWS-focused)

You will lead and contribute across the following areas:

• AWS SageMaker-based ML execution
  Designing and operating batch processing, training, and (where appropriate) inference workloads on SageMaker.
  
  
• Databricks to SageMaker migration
  Migrating Databricks notebooks, jobs, and ML workloads into containerised execution on AWS, ensuring behavioural parity and production stability.
  
  
• Python-based ML workloads
  Working directly with Python-based ML codebases (e.g. sklearn, XGBoost, and similar libraries), refactoring only where required to support containerised execution.
  
  
• Containerised ML runtimes
  Using containers to replicate Databricks runtimes, manage Python dependencies, and stabilise legacy workloads.
  
  
• ML pipelines & automation
  Orchestrating end-to-end ML workflows on AWS, including batch execution, retraining, and validation.
  
  
• Monitoring, validation & governance
  Implementing monitoring, logging, and validation patterns suitable for regulated production ML environments.
  
  

Future-state definition & collaboration

• Acting as the primary technical counterpart to Data Science and ML leadership, helping define best-practice MLOps patterns on SageMaker.
• Contributing to a future-state MLOps framework covering CI/CD, retraining strategies, monitoring, and governance.
• Balancing delivery speed with safety: prioritising “lift & shift” where required, while laying foundations for future optimisation.
  
  

Essential skills & experience (must-haves)

• Proven, hands-on experience migrating ML workloads from Databricks to AWS SageMaker (this is non-negotiable).
• Strong experience building and operating Python-based ML workloads in production environments.
• Solid understanding of container-based ML execution and Python dependency management.
• Experience leading or owning technical delivery across multiple engineers and workstreams.
• Comfort working in regulated or high-governance environments where validation, auditability, and controlled change are required.
  
  

What success looks like in this role

• All in-scope Databricks workloads are migrated and running reliably on AWS SageMaker by the agreed deadline.
• A small number of clear, repeatable migration and execution patterns are used consistently across delivery.
• Complex and legacy workloads are handled safely without blocking overall progress.
• A clear, agreed SageMaker-based MLOps direction is in place for future work.