• Remote
• Full-time
• Occasional (1x a year) travel to UK/LV
• Start date: asap
• End date: 31/12/2026 + extensions
• Huge UK media client

Overview:

The Software Engineer is responsible for developing, maintaining, and supporting the Machine Learning and AI platform. The role focuses on building shared tools, services, and automation that enable teams across the organisation to build, deploy, and operate machine learning and generative AI solutions.

Working closely with engineers, data scientists, and platform teams, the Software Engineer contributes to the delivery of secure, scalable, and maintainable platform capabilities. The role provides opportunities to develop technical skills while working with modern cloud technologies, machine learning platforms, and software engineering practices.

Roles/Responsibilities:

• Develop and maintain tooling and platform capabilities that support Data Science, MLOps, and LLMOps workflows.
• Build, deploy, and support machine learning and generative AI solutions using Amazon SageMaker, Amazon Bedrock, and other AWS services.
• Develop and maintain Infrastructure as Code (IaC) using AWS CDK, CloudFormation, or Terraform.
• Contribute to CI/CD pipelines using GitHub Actions, AWS CodePipeline, Jenkins, or similar tooling.
• Support monitoring, logging, and observability using CloudWatch, Prometheus, Grafana, or similar tools.
• Write well-tested, maintainable code following established software engineering practices, including code reviews and automated testing.
• Work with engineers, architects, product managers, and data scientists to deliver platform capabilities.
• Contribute to the implementation of platform architecture and engineering standards.
• Apply security and operational best practices throughout the software development lifecycle.
• Share knowledge within the team through documentation, pair programming, and collaborative working.

Essential:

• Experience developing software using Python or a similar programming language.
• Experience working with cloud-based platforms in AWS.
• Experience with Infrastructure as Code and CI/CD, such as AWS CDK, CloudFormation, Terraform or GitHub Actions.
• Understanding of production support, troubleshooting, monitoring and operational practices.
• Experience with version control, automated testing and software engineering best practices.

Desirable:

• Experience with Docker and Kubernetes or other container platforms.
• Experience with AWS ML/AI services such as SageMaker or Bedrock.
• Experience with MLOps/LLMOps or supporting ML/GenAI workloads.
• Experience with AWS services such as Lambda, S3, IAM, VPC, SQS or EventBridge.
• Experience with observability tools such as CloudWatch, Prometheus or Grafana.

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