ML Ops Engineer

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.

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.

Cloud & Data Engineering

You will act as the Lead MLOps Engineer for a time‑bound delivery programme focused on migrating production ML workloads from Databricks to AWS Sage Maker within a regulated environment. This role owns the technical direction, delivery integrity and coordination across all technical work streams. The immediate priority is a container‑first migration of existing Databricks‑hosted ML workloads to AWS, with Sage Maker as the default execution platform and a hard commercial deadline.

In parallel, you will help define the future MLOps operating model on Sage Maker, 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.

• 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.

• 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.

• Acting as the primary technical counterpart to Data Science and ML leadership, helping define best‑practice MLOps patterns on Sage Maker.
• Contributing…