Lead MLOps Engineer

Digital Transformation & Software Engineering consultancy immediately require a Lead MLOps Engineer on a contract basis who has expertise in Databricks and AWS SageMaker.

Contract Details:

• Job Title: Lead MLOps Engineer
• Day Rate: £625
• Location: Fully Remote
• Determination: OUTSIDE IR35
• Duration: 3 months initially (extension highly likely)
• Start Date: ASAP

Project Scope:

Time-bound delivery programme focused on migrating production ML workloads from Databricks to AWS SageMaker to a client within a regulated environment. 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.

Responsibilities:

• This is a hands-on technical leadership role where you will set patterns, review work, unblock delivery, and personally handle the most complex migrations.
• Act as the Lead MLOps Engineer delivering the migration from Databricks to AWS SageMaker.
• Own the technical direction, delivery integrity, and coordination across all technical workstreams.
• 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.

What you’ll be doing:

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.

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• 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.

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.