DevOps+MLOps+PythonML
Confirmed on the employer's own hiring board on Oct 18, 2026. First seen by Alion on Sep 19, 2026. Infosys scores A on the Alion truth index.
About the job:
Build, automate, and scale intelligent systems that move seamlessly from experimentation to reliable production. In this role, you’ll work at the intersection of DevOps and MLOps-helping teams ship ML-powered features faster, safer, and with measurable impact. You’ll partner closely with data scientists, engineers, and platform teams to create repeatable pipelines, production-grade deployments, and strong observability across environments. If you enjoy solving real-world reliability challenges, improving developer experience through automation, and enabling ML models to perform consistently in production, this is a great opportunity to grow your ownership and technical depth while contributing to a collaborative, high-learning culture.
Responsibilities
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.
Technical requirements
SKILLS:
DevOps+MLOps+PythonML
Good to have skills:
Docker, Kubernetes, Terraform, MLflow, Airflow
Additional responsibilities
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.
Preferred skills
Technology->Devops->Ansible,Technology->AI-AI Engineering->MLOps,Technology->OpenSystem->Python - OpenSystem,Technology->AI-Data science->Machine Learning
Education
MCA,MSc,MTech,Bachelor of Engineering,BTech