[Hiring] DataOps / MLOps Engineer @CXG

Role Description

We are currently looking to hire a DataOps / MLOps Engineer (Strong DevOps Focus) to work with us remotely.

Your duties will involve:

• Designing, building, and operating highly reliable, secure, and scalable DataOps and MLOps platforms with strong engineering rigor.
• Owning infrastructure, data, and ML production pipelines end to end, enforcing automation, testing, governance, and operational best practices.
• Acting as the quality and reliability gatekeeper for all data and AI workloads.

Qualifications

• 2 to 3 years of experience in DataOps and MLOps in production environments.
• Strong hands-on experience with a solid DevOps engineering background and 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.

Requirements

• Deep expertise in DataOps and MLOps principles.
• Strong DevOps skills with a rigorous, engineering-first mindset.
• Infrastructure as Code (Terraform and related tools).
• CI/CD for data and ML pipelines, including testing, validation, and versioning.
• Workflow orchestration and scheduling using Airflow.
• Containerization and orchestration (Docker, AKS/Kubernetes).
• Cloud platform management (with focus on AKS and supporting services).
• Governance and access management across the data & AI ecosystem.
• Manage repository access for Data Engineers, Analysts, and Scientists.
• Control and audit Snowflake access, roles, and permissions.
• Integrate and manage Active Directory (AD) groups and identity-based access.
• Data platform operations (Snowflake, dbt).
• ML lifecycle management (deployment, monitoring, rollback, drift detection).
• Monitoring, logging, alerting, and observability for data and ML systems.
• Security, compliance, documentation, and standardization.
• Detail-oriented, methodical, and highly rigorous approach to engineering and operations.