Overview
MLOps engineers build and maintain the infrastructure that takes machine-learning models from notebook to production at scale — training pipelines, model serving, feature stores, monitoring for drift, and continuous deployment. Distinct from ML engineers (who build models) and DevOps engineers (who handle generic infrastructure), MLOps sits at the intersection. India's AI boom has made MLOps one of the highest-demand specialisations: Razorpay, Swiggy, Flipkart, CRED, plus all major GCCs (Microsoft, Google, Amazon, Walmart) operate dedicated MLOps teams. Pay rivals senior backend engineering.
Day-to-day responsibilities
Design and maintain ML training pipelines (Kubeflow, Airflow, Vertex AI); build feature stores (Feast, Tecton); deploy models to serving infrastructure (Triton, KServe, SageMaker, BentoML); set up monitoring for model drift, data drift, and prediction quality; implement A/B testing and shadow deployment for model rollouts; manage GPU clusters and training infrastructure; collaborate with ML engineers on reproducibility; automate retraining and model evaluation; ensure compliance and audit trails for production models.
Skills required
- Python and Bash scripting
- Kubernetes and Docker
- CI/CD for ML (GitHub Actions, Argo, Jenkins)
- Pipeline orchestration (Airflow, Kubeflow, Dagster)
- Model serving (Triton, BentoML, SageMaker, KServe)
- Feature stores (Feast, Tecton, Hopsworks)
- Cloud ML platforms (Vertex AI, SageMaker, Azure ML)
- Monitoring tools (Prometheus, Grafana, Evidently, WhyLabs)
Education path
B.Tech CSE/IT, BCA, M.Tech CS, or M.Sc Data Science. Transition path: many MLOps engineers come from software engineering or DevOps backgrounds via 1-2 years of ML platform work. Self-taught entry common — open-source contributions to Kubeflow, MLflow, Feast, etc. signal strongly at hiring. Cloud ML certifications (Google Cloud ML Engineer, AWS Machine Learning Specialty) are widely valued.
Career progression
- 1MLOps Engineer (0-2 yrs)
- 2Senior MLOps Engineer (2-5 yrs)
- 3Staff MLOps Engineer (5-9 yrs)
- 4Principal MLOps Engineer (9-13 yrs)
- 5ML Platform Architect / VP (13+ yrs)
Top employers in India
Useful certifications
- Google Cloud Professional ML Engineer
- AWS Certified Machine Learning Specialty
- Kubernetes CKAD / CKA
- MLflow practitioner
- Linux Foundation MLOps courses