Posted 15h ago
ML OPS Devops
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What this role pays, in context
This listing pays 420% above the median for DevOps Engineer roles in India, which sits at ₹4.9L a year.
25th pct ₹4.3L75th pct ₹5.6L
Median of 16 other live DevOps Engineer listings in India on this board that state pay. Roles that do not disclose a salary are excluded rather than counted as zero.
We are seeking a Senior MLOps Engineer to build and maintain the infrastructure that takes machine learning models from experimentation to production. You will design robust CI/CD pipelines for ML workflows, automate model training and deployment, and ensure our AI systems are reliable, observable, and scalable. This role sits at the intersection of software engineering, DevOps, and data science.
Responsibilities
- • Design, build, and operate end-to-end ML pipelines — from data ingestion and feature engineering to model training, validation, and serving.
- • Implement CI/CD workflows for ML models including automated testing, versioning, and canary deployments.
- • Manage and optimize model serving infrastructure (batch and real-time) for latency, throughput, and cost.
- • Build monitoring and alerting systems for data drift, model performance degradation, and infrastructure health.
- • Maintain experiment tracking platforms and model registries to ensure reproducibility and governance.
- • Automate infrastructure provisioning using IaC tools (Terraform, Pulumi, CloudFormation).
- • Collaborate with data scientists to containerize models and standardize training environments.
- • Implement feature stores, data versioning, and lineage tracking for ML data assets.
Requirements
- 5–8 years of experience in DevOps, platform engineering, or MLOps, with significant focus on ML systems.
- • Strong programming skills in Python; working knowledge of Go, Bash, or similar.
- • Hands-on experience with container orchestration (Kubernetes, Docker) and cloud services (AWS, GCP, or Azure).
- • Proficiency with CI/CD tools (GitHub
- Actions, GitLab CI, Jenkins, Argo Workflows, or similar).
- • Experience with ML pipeline frameworks (Kubeflow, Airflow, Prefect, Metaflow, or equivalent).
- • Familiarity with model serving tools (TorchServe, Triton, TF Serving, Seldon, BentoML, or similar).
- • Solid understanding of monitoring and observability (Prometheus, Grafana, Datadog, or equivalent).
- • Bachelor's degree in computer science, Software Engineering, or a related field. • Experience building or managing GPU clusters for training workloads.
- • Familiarity with feature store platforms (Feast, Tecton, or equivalent).