DevOps and MLOps Engineer with Python and Machine Learning Expertise

Infosys; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Infosys in Bengaluru seeks a DevOps+MLOps+PythonML Developer to design and implement robust CI/CD pipelines for ML services across environments, ensuring scalable and reliable production systems.

You will automate infrastructure provisioning, establish observability, and collaborate with data science and engineering teams to productionize ML models and LLM workloads with emphasis on reliability and cost awareness.

Qualifications

• Bachelor’s degree or equivalent in Engineering/Technology/Computer Science (BTech/BE or equivalent) or higher.
• 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.

Responsibilities

• Design, implement, and maintain CI/CD pipelines for applications and ML services across environments.
• Automate infrastructure provisioning and configuration to improve reliability and deployment speed.
• Establish monitoring, logging, and alerting practices to improve observability and incident response.
• Ensure secure access controls, secrets management, and environment hygiene across development and production.
• Build and maintain ML pipelines for training, validation, packaging, and deployment of models using Python-based workflows.
• Enable model versioning, reproducibility, and controlled rollouts for ML services.
• Collaborate 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.
• Support deployment patterns for LLM-based services, including scalable inference and runtime monitoring.

Skills

DevOps

MLOps

Python ML

Education

BTech/BE / CS

MTech/MCA/MSc

Tools

Kubernetes

Docker

Terraform

MLflow

Apache Airflow

Job description

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.