Key Responsibilities:

• Manage and optimize data warehouse operations, ensuring high availability and performance.
• Debug and maintain Informatica, SLJM, and Apache Airflow jobs and workflows.
• Deploy, monitor, and operationalize ML models, including retraining and performance evaluation.
• Design and develop scalable MLOps frameworks for model deployment, tracking, and governance.
• Build and maintain CI/CD pipelines to automate data and model workflows using Docker and Kubernetes.
• Collaborate with data scientists, data engineers, and software developers to ensure smooth integration and delivery of AI/ML solutions.
• Continuously improve automation, monitoring, and alerting across DevOps and MLOps environments.

Required Qualifications:

• Education: Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
• Experience: 1–3 years of experience in DevOps, DataOps, or MLOps roles.
• Technical Skills:
• Strong understanding of data warehouse operations.
• Hands-on experience with Informatica, SLJM, and Apache Airflow.
• Practical knowledge of ML model deployment and monitoring.
• Proficiency in containerization and orchestration tools — Docker and Kubernetes.
• Familiarity with CI/CD pipelines and version control (Git, Jenkins, etc.).
• Good scripting and automation skills (Python, Bash, etc.).

Preferred Qualifications:

• Exposure to cloud platforms (AWS, Azure, or GCP).
• Knowledge of MLOps tools (MLflow, Kubeflow, Airflow DAGs for ML pipelines).
• Relevant certifications such as AWS DevOps Engineer or Azure DevOps Engineer.