Machine Learning Engineer — MLOps

Role: Machine Learning Engineer — MLOps

Build and maintain production-ready ML systems, ensuring models are reproducible, monitored, and safely deployed.

Responsibilities

• Design end-to-end MLOps pipelines: data ingestion, training, validation, and deployment
• Implement model CI/CD with automated testing
• Add safer release mechanisms such as canary and rollback strategies
• Build monitoring for model performance, data drift, and alerting
• Collaborate with infra teams to improve cost, scaling, and reproducibility of model workloads

Requirements

• 3+ years in ML engineering or SRE experience building MLOps pipelines
• Experience with orchestration tools: Airflow or Kubeflow
• Familiarity with infrastructure-as-code and CI systems
• Experience with model serving frameworks: TorchServe, TensorFlow Serving (TF Serving), or NVIDIA Triton
• Strong Python skills
• Cloud experience with AWS, GCP, or Azure

Nice to Haves

• Experience with observability tooling for production ML systems (implied by monitoring and alerting responsibilities)

Work Model & Location

• Remote / In-house (Hybrid)

Salary

• ₹110k–₹180k INR (depending on experience)