DevOps+MLOps+PythonML

About the job: Build, automate, and scale intelligent systems that move seamlessly from experimentation to reliable production. In this role, you’ll work at the intersection of DevOps and MLOps—helping teams ship ML-powered features faster, safer, and with measurable impact. You’ll partner closely with data scientists, engineers, and platform teams to create repeatable pipelines, production-grade deployments, and strong observability across environments. If you enjoy solving real-world reliability challenges, improving developer experience through automation, and enabling ML models to perform consistently in production, this is a great opportunity to grow your ownership and technical depth while contributing to a collaborative, high-learning culture. 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).
• 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. Technical requirements SKILLS: DevOps+MLOps+PythonML Good to have skills: Docker, Kubernetes, Terraform, MLflow, Airflow 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. Education MCA,MSc,MTech,Bachelor of Engineering,BTech