Senior DevOps / Platform Engineer (AWS, Python, MLOps)

Overview
We are looking for a Senior DevOps / Platform Engineer to design, build, and maintain scalable, secure, and reliable cloud infrastructure supporting Machine Learning and Generative AI workloads.
The role focuses primarily on DevOps and Platform Engineering, including AWS infrastructure, Infrastructure as Code (IaC), CI/CD automation, containerisation, monitoring, security, and production operations. Software development with Python is also an important part of the role, particularly for building internal platform tools, automation services, and reusable components.
You will work closely with software engineers, data scientists, ML engineers, and architects to improve platform capabilities and enable teams to develop, deploy, and operate ML and GenAI solutions efficiently.
Candidates with strong DevOps or Platform Engineering backgrounds and Python development experience are welcome, as are Senior MLOps Engineers with solid cloud infrastructure and software engineering skills.
Key Responsibilities
Design, build, and maintain scalable AWS infrastructure for production applications and ML/GenAI workloads.
Develop and maintain Infrastructure as Code (IaC) using Terraform, AWS CDK, or CloudFormation.
Build, maintain, and optimise CI/CD pipelines using GitHub Actions, AWS CodePipeline, Jenkins, or similar tools.
Automate infrastructure provisioning, deployment processes, and operational tasks using Python and scripting.
Manage containerised environments using Docker and Kubernetes or similar technologies.
Implement monitoring, logging, alerting, and observability using Amazon CloudWatch, Prometheus, Grafana, and related tools.
Support and troubleshoot production systems, ensuring reliability, availability, performance, and security.
Apply AWS security best practices and DevSecOps principles throughout the software development lifecycle.
Develop and maintain Python-based platform tools, automation services, and reusable components.
Support the deployment and operation of ML and Generative AI workloads using AWS services, including Amazon SageMaker and Amazon Bedrock.
Collaborate with engineering, Data Science, and ML teams to improve platform reliability, scalability, and developer experience.
Contribute to technical standards, documentation, code reviews, and continuous improvement initiatives.
Essential Requirements
Strong experience in DevOps, Platform Engineering, Cloud Infrastructure, or MLOps engineering.
Strong hands-on experience with AWS cloud services and production environments.
Proficiency in Python, including scripting, automation, or development of internal tools and services.
Practical experience with Infrastructure as Code, using Terraform, AWS CDK, CloudFormation, or similar tools.
Experience building and maintaining CI/CD pipelines using GitHub Actions, Jenkins, AWS CodePipeline, or equivalent technologies.
Experience with Docker, Kubernetes, or similar containerisation technologies.
Understanding of cloud networking, infrastructure security, and access management.
Experience with production system monitoring, troubleshooting, logging, and observability.
Good understanding of software engineering practices, including version control, automated testing, code reviews, and reliable deployment processes.
Ability to collaborate with cross-functional engineering teams and contribute to scalable platform solutions.
Desirable Requirements
Experience with MLOps, LLMOps, or operating ML/GenAI workloads in production.
Hands-on experience with Amazon SageMaker, Amazon Bedrock, or other AWS AI/ML services.
Familiarity with ML lifecycle and orchestration tools such as SageMaker Pipelines, MLflow, or similar platforms.
Experience with serverless and event-driven architectures using AWS Lambda, SQS, EventBridge, or related services.
Experience implementing DevSecOps practices and automated security checks.
Experience designing and maintaining internal developer platforms, shared infrastructure, or reusable engineering tools.
Familiarity with monitoring and operating AI/ML pipelines and services in production.
Ideal Candidate Profile
We are particularly interested in candidates coming from one of the following backgrounds:
Senior DevOps Engineer with strong AWS, Infrastructure as Code, CI/CD, and Python experience.
Senior Platform Engineer experienced in building and maintaining cloud platforms, automation, and developer tooling.
Senior MLOps Engineer with strong AWS infrastructure, deployment automation, and production operations experience.