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. Technical Requirements: SKILLS: DevOps+MLOps+PythonML Good to have skills: Docker, Kubernetes, Terraform, MLflow, Airflow 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. Preferred Skills: Technology->Devops->Ansible,Technology->AI-AI Engineering->MLOps,Technology->OpenSystem->Python - OpenSystem,Technology->AI-Data science->Machine Learning