Key skills
AirflowApacheDockerKubernetesTerraformAIMLMLOpsSnowflakeApache AirflowdbtAKSCI/CD
About this role
Role Overview
- Designs, builds, and operates highly reliable, secure, and scalable DataOps and MLOps platforms with strong engineering rigor.
- Owns infrastructure, data, and ML production pipelines end to end, enforcing automation, testing, governance, and operational best practices.
- Acts as the quality and reliability gatekeeper for all data and AI workloads.
Requirements
- The ideal experience range for this role is 2 to 3 years
- Strong hands-on experience in DataOps and MLOps in production environments
- Solid DevOps engineering background with an automation-first mindset
- Experience with Infrastructure as Code using Terraform
- Expertise in CI/CD for data and ML pipelines, including testing and versioning
- Workflow orchestration using Apache Airflow
- Containerization and orchestration with Docker and Kubernetes (AKS preferred)
- Experience operating data platforms such as Snowflake and dbt
- Knowledge of ML lifecycle management (deployment, monitoring, rollback, drift detection)
- Experience with access management, governance, and security across data and AI systems
- Strong focus on monitoring, reliability, and operational best practices
- Detail-oriented with a structured and rigorous engineering approach
Tech Stack
- Airflow
- Apache
- Docker
- Kubernetes
- Terraform