DevOps+MLOps+PythonML Developer

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.

Additional Responsibilities

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

Technical and Professional 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

Preferred Skills

• Continuous delivery – Continuous deployment and release
• MLOps
• PYTHON
• Machine Learning

Educational Requirements

MCA,MSc,MTech,Bachelor of Engineering,BTech