DevOps and MLOps Engineer

Infosys; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Infosys in Bengaluru is seeking a hands-on DevOps/MLOps Engineer to drive ML lifecycle automation and scalable platform delivery. You will design and maintain CI/CD pipelines, containerized deployments, and infrastructure automation to support ML services and Python-based workloads.

You will collaborate with data scientists to productionize code, set standards for logging, metrics, and runbooks, and contribute to reliable, cost-efficient engineering practices.

Qualifications

• 2–3 years hands-on experience in DevOps and/or MLOps-focused roles.
• CI/CD concepts and deployment/release automation experience.
• Experience supporting Python-based ML workloads (packaging, environments, runtime troubleshooting).
• Strong Linux fundamentals, networking basics, and system troubleshooting.

Responsibilities

• Design and maintain CI/CD workflows to automate ML and deployment processes.
• Implement infrastructure automation and configuration management for consistent dev/stage/prod environments.
• Improve system reliability through monitoring, alerting, incident response, and post-incident improvements.
• Build ML pipelines for training, validation, packaging, and deployment with reproducibility and traceability.
• Enable model versioning, artifact management, and controlled rollouts (canary/blue-green) for ML services.
• Establish model performance monitoring, drift detection signals, and feedback loops for continuous improvement.
• Work with data science teams to productionize Python ML code with robust testing, packaging, and runtime optimization.
• Define operational standards (logging, metrics, SLOs) and contribute to documentation and runbooks.
• Participate in code reviews and propose improvements to security, scalability, and cost efficiency.

Skills

DevOps

MLOps

Python ML

Education

BTECH / MTECH / MCA / MSC (or equivalent practical experience)

Tools

Docker

Kubernetes

Terraform

MLflow

Airflow

Job description

DevOps+MLOps+PythonML SKILLS:

DevOps+MLOps+PythonML Good to have skills: Docker, Kubernetes, Terraform, MLflow, Airflow

Preferred Qualifications

• Experience productionizing ML workflows end-to-end (training pipelines, model registry/artifacts, deployment, monitoring).
• Exposure to containerization and orchestration for scalable ML services (e.g., Docker, Kubernetes).
• Familiarity with Infrastructure as Code and configuration tools (e.g., Terraform, Ansible).
• Experience with ML lifecycle tooling (e.g., MLflow, Kubeflow) and workflow orchestration (e.g., Airflow).
• Hands-on exposure to LLM-enabled applications, including deployment patterns, inference optimization, and evaluation/monitoring approaches.
• Strong communication skills to align platform practices across engineering and data science stakeholders.

Key Responsibilities

Platform & Automation

• Design and maintain CI/CD workflows to automate build, test, release, and deployment processes for ML and supporting services.
• Implement infrastructure automation and configuration management to ensure consistent environments across dev, staging, and production.
• Improve system reliability through monitoring, alerting, incident response practices, and post-incident improvements.

MLOps & Model Delivery

• Build and manage ML pipelines for training, validation, packaging, and deployment with reproducibility and traceability.
• Enable model versioning, artifact management, and controlled rollouts (e.g., canary/blue-green) for ML services.
• Establish model performance monitoring, drift detection signals, and feedback loops for continuous improvement.

Collaboration & Engineering Excellence

• Work with data science teams to productionize Python ML code with robust testing, packaging, and runtime optimization.
• Define operational standards (logging, metrics, SLOs) and contribute to documentation and runbooks.
• Participate in code reviews and propose improvements to security, scalability, and cost efficiency.

Minimum Qualifications

• BTECH / MTECH / MCA / MSC (or equivalent practical experience).
• 2–3 years of hands‑on experience in DevOps and/or MLOps-focused engineering roles.
• Working experience with CI/CD concepts and automation for deployments and releases.
• Practical experience supporting Python-based ML workloads (packaging, environments, dependency management, runtime troubleshooting).
• Strong understanding of Linux fundamentals, networking basics, and system troubleshooting.