SKILLS: DevOps+MLOps+PythonML Good to have skills: Docker, Kubernetes, Terraform, MLflow, Airflow

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).
- 2–3 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.

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.
- Robust communication skills to align platform practices across engineering and data science stakeholders.

📌 DevOps+MLOps+PythonML (Bengaluru)
🏢 Infosys
📍 Bengaluru