ML Platform Lead Engineer
LSEG (London Stock Exchange Group) (company site, opens in a new tab)5+ yrsOn-siteDirect career pageEst. 22–35 LPA
Posted on 9 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design, build, automate, and support cloud infrastructure and deployment pipelines for a centralized Data Science and AI/ML platform on AWS.
- Manage highly available, secure, and scalable AWS infrastructure using Infrastructure as Code including Terraform and Python automation.
- Build and optimize CI/CD pipelines (GitLab or equivalent), containerize applications with Docker and Kubernetes, and promote DevOps and infrastructure standardization across teams.
Minimum Requirements
- 5+ years of Python experience in automation, deployment utilities, infrastructure tooling, and operational automation.
- 3+ years of Terraform experience provisioning and managing AWS infrastructure, including reusable modules and remote state management.
- Strong hands-on AWS experience including SageMaker, Bedrock, EC2, VPC, IAM, S3, RDS, EKS.
- Experience with Docker containerization and Kubernetes cluster management, including Helm charts, networking, autoscaling, and rolling deployments.
Ideal Candidate Profile
- Experienced in cloud-native AI/ML platform engineering with strong expertise in AWS services relevant to Data Science/ML workloads.
- Skilled in end-to-end DevOps practices including CI/CD pipeline automation and Infrastructure as Code in a multi-team federated engineering environment.
- Comfortable leading platform engineering efforts that emphasize automation, operational excellence, security, and scalability for financial market infrastructure.