DevOps+MLOps+PythonML Developer

About the job: Join a fast-moving team where you’ll help shape reliable, scalable, and secure platforms that power modern machine learning solutions. In this role, you’ll blend strong DevOps practices with MLOps execution and Python-based ML workflows to ensure models move smoothly from experimentation to production—without compromising performance, observability, or governance. You’ll collaborate closely with data scientists, engineers, and stakeholders to automate pipelines, standardize deployments, and improve the end-to-end lifecycle of ML systems. If you enjoy solving real-world delivery challenges, building repeatable automation, and enabling teams to ship ML features confidently, this opportunity offers hands-on ownership, continuous learning, and a culture that values collaboration and practical innovation. Technical Requirements: • Primary skills: DevOps/MLOps/PythonML -Domain->Turbomachinery->Compressor->Rotor,Technology->Data Science->Machine Learning,Technology->DevOps->Continuous delivery - Continuous deployment and release,Technology->Machine Learning->Python Responsibilities: Key Responsibilities: DevOps & Platform Enablement • Design, implement, and maintain CI/CD pipelines for applications and ML services across environments. • Automate infrastructure provisioning and configuration to improve reliability, repeatability, and deployment speed. • Establish monitoring, logging, and alerting practices to improve system observability and incident response. • Ensure secure access controls, secrets management, and environment hygiene across development and production. MLOps & ML Delivery • Build and maintain ML pipelines for training, validation, packaging, and deployment of models using Python-based workflows. • Enable model versioning, reproducibility, and controlled rollouts (e.g., canary/blue-green) for ML services. • Partner with data science teams to productionize models and define operational SLAs for ML endpoints and batch jobs. • Implement automated quality checks for data/model artifacts to reduce regressions and improve release confidence. LLM Enablement • Support deployment patterns for LLM-based services, including scalable inference, prompt/version management, and runtime monitoring. • Collaborate on integrating LLM capabilities into existing platforms with a focus on reliability, latency, and cost awareness. Preferred Skills: Technology->DevOps->Continuous delivery - Continuous deployment and release,Technology->AI-AI Engineering->MLOps,Technology->AI-Data science->PYTHON,Technology->AI-Data science->Machine Learning