Job Description

Responsibilities :

Key Responsibilities: Platform & Automation - Design and maintain CI/CD workflows to automate build, test, release, and deployment processes for ML and supporting services. - Implement infrastructure automation and configuration management to ensure consistent environments across dev, staging, and production. - Improve system reliability through monitoring, alerting, incident response practices, and post-incident improvements. MLOps & Model Delivery - Build and manage ML pipelines for training, validation, packaging, and deployment with reproducibility and traceability. - Enable model versioning, artifact management, and controlled rollouts (e.g., canary/blue-green) for ML services. - Establish model performance monitoring, drift detection signals, and feedback loops for continuous improvement. Collaboration & Engineering Excellence - Work with data science teams to productionize Python ML code with robust testing, packaging, and runtime optimization. - Define operational standards (logging, metrics, SLOs) and contribute to documentation and runbooks. - Participate in code reviews and propose improvements to security, scalability, and cost efficiency. Minimum Qualifications: - BTECH / MTECH / MCA / MSC (or equivalent practical experience). - 2â€3 years of hands-on experience in DevOps and/or MLOps-focused engineering roles. - Working experience with CI/CD concepts and automation for deployments and releases. - Practical experience supporting Python-based ML workloads (packaging, environments, dependency management, runtime troubleshooting). - Strong understanding of Linux fundamentals, networking basics, and system troubleshooting.

Additional Responsibilities:

Preferred Qualifications: - Experience productionizing ML workflows end-to-end (training pipelines, model registry/artifacts, deployment, monitoring). - Exposure to containerization and orchestration for scalable ML services (e.g., Docker, Kubernetes). - Familiarity with Infrastructure as Code and configuration tools (e.g., Terraform, Ansible). - Experience with ML lifecycle tooling (e.g., MLflow, Kubeflow) and workflow orchestration (e.g., Airflow). - Hands-on exposure to LLM-enabled applications, including deployment patterns, inference optimization, and evaluation/monitoring approaches. - Strong communication skills to align platform practices across engineering and data science stakeholders.