Engineer lead

Join an innovative organisation investing in modern machine learning capabilities and cloud-first engineering. This is a key leadership role where you'll shape the MLOps foundations that enable multiple teams to build, deploy, and manage production-ready ML solutions at scale.

Role Overview

• Location: Fully Remote in the UK
• Contract Day Rate: Competitive - Outside IR35
• Industry: Technology / Data & AI

What You’ll Be Doing?

• Design, build, and maintain a scalable MLOps platform using Amazon SageMaker, covering model training, deployment, pipelines, monitoring, and governance.
• Lead the migration of a complex suite of production machine learning models from legacy platforms into SageMaker, ensuring successful delivery and production readiness.
• Develop and manage CI/CD pipelines that automate model testing, validation, and promotion across multiple environments.
• Define secure cloud standards, including IAM permissions, encryption, and networking controls for machine learning workloads.
• Establish reusable MLOps templates, standards, and best practices that allow engineering and data science teams to self-serve confidently.
• Implement robust model governance, monitoring, drift detection, and automated retraining processes.
• Produce clear technical documentation and operational runbooks to support long-term platform adoption.
• Work closely with data scientists, platform engineers, and security teams to coordinate successful delivery across multiple workstreams.
• Communicate technical risks, migration progress, and governance decisions to both technical and non-technical stakeholders.
• Take ownership of technical direction, making informed decisions in complex environments while adapting as new challenges emerge.

Main Skills Needed?

• Expert-level experience with Amazon SageMaker, including Studio, Training, Pipelines, Endpoints, and production MLOps practices.
• Strong AWS knowledge across IAM, S3, KMS, and CI/CD tooling such as CodePipeline, CodeBuild, or equivalent.
• Expert Python development skills, with PySpark experience highly desirable.
• Proven experience designing enterprise MLOps frameworks, including model registries, monitoring, governance, and deployment automation.
• Strong understanding of statistical validation and model parity testing methodologies.
• Advanced Git and version control experience.
• Knowledge of Infrastructure as Code using Terraform, CloudFormation, or CDK is advantageous.
• Familiarity with AWS services including Step Functions, Lambda, CloudWatch, CloudTrail, Glue, EMR, Lake Formation, Feature Store, and VPC networking would be beneficial.
• Experience with data governance, security, and compliance within cloud environments.
• Ability to lead technical strategy, mentor teams, manage competing priorities, and communicate effectively with stakeholders at every level.

What’s in It for You?

• The opportunity to define the engineering standards that multiple teams will build upon.
• A highly visible leadership role with genuine technical ownership.
• Work on large-scale machine learning transformation projects using modern AWS technologies.
• Collaborate with experienced data science, engineering, and cloud specialists.
• Influence platform direction, architecture, and engineering best practice across the wider business.
• A supportive environment that values knowledge sharing, continuous improvement, and technical excellence.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.