We're looking for an experienced MLOps Engineer to drive ML automation, deployment and platform reliability. Senior MLOps Engineer (DevOps) | 6-9 Y | Bangalore (Hybrid) 3 Days Office | Quick Joiner | Key Technical
Skills:
DevOps & Cloud Infrastructure Python, Bash / Go Docker and Kubernetes AWS / Azure / GCP Terraform, Pulumi or CloudFormation CI/CD & ML Pipelines GitHub Actions, GitLab CI, Jenkins or Argo Workflows Kubeflow, Airflow, Prefect or Metaflow Automated model testing, versioning and deployment Canary deployments and ML workflow automation Model Deployment & Serving TorchServe, NVIDIA Triton, TensorFlow Serving Seldon, BentoML or similar Batch and real-time model serving Monitoring & MLOps Prometheus, Grafana, Datadog MLflow, Weights & Biases or Neptune Model monitoring, data drift and performance tracking Feature stores – Feast / Tecton Data versioning – DVC / LakeFS
Key Responsibilities
Build and manage end-to-end ML pipelines. Automate model training,
validation and deployment. Develop robust CI/CD pipelines for ML workflows. Manage scalable Kubernetes-based ML infrastructure and GPU clusters. Implement model monitoring, observability and alerting. Maintain experiment tracking, model registries and data lineage. Optimize cloud infrastructure performance and costs. Collaborate with Data Science teams to accelerate production AI/ML deployments. Valuable to Have: Experience with GPU infrastructure and distributed ML workloads. Exposure to LLM deployment, Vector Databases and RAG infrastructure. Experience with feature stores and data governance. Ideal Candidate: A hands-on MLOps / DevOps professional with strong cloud, Kubernetes, CI/CD and ML infrastructure expertise, capable of building reliable and scalable production ML platforms.