MLOps Engineer

9 hours ago

Chennai Tamil Nadu India, Tamil Nadu EROS GenAI Full time

Role Overview

We are looking for an experienced MLOps Engineer to build and scale our AI infrastructure across Kubernetes, cloud-native environments, and serverless GPU platforms. You will own the end-to-end operational lifecycle of machine learning models—from training to deployment, monitoring, optimization, and automated retraining.


Key Roles

• Design and implement highly scalable AI/ML infrastructure using Kubernetes, Kubeflow, Ray, and cloud-native services.

• Build robust CI/CD and CT (Continuous Training) pipelines for model deployment, inference, monitoring, and automated retraining.

• Architect and deploy ML workflows on serverless GPU platforms (AWS, GCP, Yotta, RunPod, Modal, etc.) for cost-efficient, elastic scaling.

• Establish automated systems for model drift, data drift, performance monitoring, and lineage tracking.

• Promote best practices in reproducible ML, infrastructure-as-code, automation, and internal tooling.


Responsibilities

• Evaluate, integrate, and optimize MLOps tools (MLflow, Weights & Biases, KServe, Seldon, BentoML, Argo, Airflow, etc.) to streamline AI development.

• Develop scalable inference-serving layers—batch, real-time, streaming—using GPU-optimized serving frameworks.

• Build observability stacks for GPU utilization, latency, throughput, and model health metrics.

• Implement robust systems for model governance, versioning, rollout strategies (blue/green, canary), and automated rollback.

• Collaborate closely with ML engineers, data engineers, and product teams to deliver production-ready AI features.


Knowledge & Skills Requirements

• Strong understanding of ML/DL fundamentals and hands-on experience with model training and optimization.

• Expertise in Kubernetes, containerization, Helm, and cloud-native infrastructure.

• Experience with serverless GPU architectures and distributed computing frameworks.

• Solid knowledge of CI/CD tools (GitHub Actions, GitLab CI, Jenkins), IaC (Terraform), and workflow engines.

  • • Understanding of drift detection, performance tracking, experiment management, and scalable model deployment patterns.
  • We Accept International Applicants

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