DevOps+MLOps+PythonML

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.

Education

MCA,MSc,MTech,Bachelor of Engineering,BTech