DevOps+MLOps+PythonML Developer

DevOps+MLOps+PythonML Developer

• 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 Minimum Qualifications: • Bachelor’s degree or equivalent in Engineering/Technology/Computer Science (BTech/BE or equivalent); Master’s (MTech/MCA/MSc) is acceptable as listed. • 3–5 years of experience in DevOps and MLOps-focused delivery for production systems. • Hands-on experience with Python-based ML workflows and operationalizing ML models into services or batch pipelines. • Strong understanding of CI/CD concepts, release management, and environment promotion strategies. • Experience implementing monitoring and operational practices for reliability and troubleshooting in production. Preferred Qualifications: • Experience building and operating end-to-end MLOps pipelines including model packaging, deployment automation, and lifecycle governance. • Practical exposure to LLM solution delivery, including inference deployment, prompt iteration workflows, and evaluation/monitoring approaches. • Familiarity with containerization and orchestration for ML workloads, and optimizing deployments for performance and scalability. • Experience with infrastructure automation and configuration management to support repeatable ML environments. • Proven ability to collaborate across data science and engineering teams, translating experimentation needs into production-grade systems. Good to have skills: Kubernetes, Docker, Terraform, MLflow, Apache Airflow 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.