Software Engineer ML/Gen AI Platform Support
Summary
Senior engineer who designs, builds, and maintains an internal Machine Learning and Generative AI platform on AWS (SageMaker, Bedrock), creating shared tooling, IaC, CI/CD, and observability that lets other teams build, deploy, and operate ML and GenAI solutions at scale.
Overview
The Senior Engineer is responsible for designing, building, and maintaining the Machine Learning and AI platform. The role focuses on developing shared tools, services, and engineering patterns that enable teams across the organisation to build, deploy, and operate machine learning and generative AI solutions.
Working closely with engineering, data science, and platform teams, the Senior Engineer contributes to the technical direction of the platform, helping to deliver secure, scalable, and maintainable solutions. The role supports engineering best practices through collaboration, mentoring, and continuous improvement while contributing to the ongoing evolution of the platform.
Roles/Responsibilities
Design, develop, and maintain tooling and platform capabilities that support scalable Data Science, MLOps, and LLMOps workflows.
Build, deploy, and support machine learning and generative AI solutions using Amazon SageMaker, Amazon Bedrock, and related AWS services.
Develop and maintain Infrastructure as Code (IaC) using AWS CDK, CloudFormation.
Build and maintain CI/CD pipelines using GitHub Actions, AWS CodePipeline, Jenkins, or similar tooling.
Implement monitoring, logging, and observability solutions using tools such as CloudWatch, Prometheus, and Grafana.
Apply software engineering best practices, including automated testing, code reviews, and continuous integration.
Work with architects, product managers, and engineers to deliver scalable, secure, and maintainable platform capabilities.
Support the implementation of platform architecture and engineering standards.
Apply security best practices throughout the software development lifecycle using AWS-native services and DevSecOps principles.
Share knowledge within the team through mentoring, documentation, pair programming, and technical discussions.
Essential
Strong software engineering experience, with proficiency in Python or a similar programming language.
Strong experience developing and operating cloud-based platforms in AWS.
Experience with Infrastructure as Code, such as AWS CDK, CloudFormation or Terraform, and automated software delivery / CI/CD.
Experience supporting and troubleshooting production systems, with a good understanding of reliability, security, monitoring and operational practices.
Experience working with cloud networking and containerised environments, with an understanding of Docker and Kubernetes or similar technologies.
Desirable
Hands-on experience with AWS ML/AI services such as SageMaker and Bedrock.
Experience with MLOps/LLMOps or supporting ML/GenAI workloads in production.
Experience with ML platform tooling such as SageMaker Pipelines, MLflow or similar.
Experience with event-driven/serverless architectures using services such as Lambda, SQS or EventBridge.
Experience designing or contributing to scalable platform architectures and technical standards.