- 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

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.

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.

Positive to have skills: Kubernetes, Docker, Terraform, MLflow, Apache Airflow

📌 DevOps+MLOps+PythonML Developer (Bengaluru)
🏢 Infosys
📍 Bengaluru