Lead DevOps Engineer
Core
Designing and developing responsible AI models on an analytics platform and implementing CI/CD pipelines for efficient continuous delivery.
Role type
Lead DevOps Engineer (Data & AI)
Builds
Advanced AI model industrialization platform, microservices for scientific model deployment, and CI/CD pipelines.
Domain
Cloud-native AI and Data Engineering on AWS
Deliverable
production ML models | infrastructure
Required skills
AWS DevOps tools (CodePipeline, CodeBuild, CodeDeploy, CloudFormation, CloudWatch), Python development (FastAPI, Click, Poetry), Data Engineering (PySpark, Delta Lake), Microservices architecture, CI/CD pipeline design, Cloud-native architecture design, Code scaffolding, Quality integration (PyTest, SonarQube)
Preferred skills
Terraform, AWS CDK, Docker, Bash/Python scripting, AWS SageMaker (Experiments, Model Registry, Pipelines, Endpoints, MLflow), Additional AWS services (IAM, Secrets Manager, S3, Lambda, CloudTrail, Step Functions, API Gateway), Athena, Redshift, DynamoDB, RDS, GitFlow, Visual Studio Code
Responsibilities
Implement CI/CD pipelines for code integration and continuous delivery in AWS, Adapt existing analytics platform for responsible AI model creation, Design cloud-native architectures and configure initial scaffolding for new projects, Develop microservices oriented to deployment and consumption of scientific models, Build code archetypes integrating scientific process services for AI and Generative AI
Seniority
Mid-level (2–3 years), Hands-on IC
