Machine Learning Platform Engineer | AWS, Kubernetes, MLOps

Sector: Financial Services
Type: Permanent
Working arrangement: Hybrid
Salary: Up to £ (DOE)

Training a machine learning model is only part of the challenge. Deploying hundreds of models securely, reliably and consistently across a regulated enterprise requires a fundamentally different level of engineering.

Cedar Technology is supporting a major financial services organisation investing in the next generation of its enterprise machine learning infrastructure.

The business is developing a shared ML platform designed to give data scientists and machine learning engineers the tools, infrastructure and governance required to deploy models into production at scale.

This is a platform engineering position with a genuine focus on ML workloads, rather than a conventional Dev Ops role with an AI label attached.

The engineering challenge

• Build and evolve cloud-native ML infrastructure using AWS and Kubernetes.
• Develop scalable environments for model training, deployment and inference.
• Improve developer experience through automation, reusable tooling and self-service capabilities.
• Support orchestration and distributed workloads using technologies such as Kubeflow and Ray.
• Establish reliable CI/CD workflows, observability and deployment processes.
• Balance engineering productivity against the security, resilience and governance requirements of a regulated environment.

What we’re looking for

You’ll bring strong foundations in platform engineering, infrastructure or software engineering, alongside practical experience supporting production ML workloads.

AWS, Kubernetes, infrastructure as code, Python and CI/CD experience are particularly relevant.

Experience with model serving, distributed training, ML orchestration or MLOps would strengthen your application.

Importantly, we’re looking for engineers who understand engineering trade-offs: reliability versus performance, standardisation versus flexibility, and platform abstraction versus developer control.

Why Cedar Technology?

Our Applied AI and ML Engineering practice distinguishes between building models, engineering ML infrastructure and operating production AI systems. We assess candidates against the specific technical problems the team needs to solve.

If you’ve built the infrastructure behind production machine learning and want to influence how an enterprise scales AI, we’d welcome a conversation.

Tagged as:
Financial Services, Machine Learning, Platform Engineering