ML Ops Engineer

Overview

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.

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

Lead MLOps Engineer (Contractor)
Department:
Cloud & Data Engineering

Technical leadership & delivery ownership

• Acting as the overall technical authority for the programme, owning architectural decisions, execution patterns, and technical quality across all work streams.
• Defining and enforcing standard migration patterns for moving ML workloads from Databricks into AWS Sage Maker, 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 Sage Maker-based ML execution:
  Designing and operating batch processing, training, and (where appropriate) inference workloads on Sage Maker.
• Databricks to Sage Maker 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 Sage Maker.
• 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.

• Proven, hands-on experience migrating ML workloads from Databricks to AWS Sage Maker (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 work streams.
• Comfort working in regulated or high-governance environments where validation, auditability, and controlled change are required.

• All in-scope Databricks workloads are migrated and running reliably on AWS Sage Maker 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,…