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4-12 Years
Not Disclosed
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  • Posted 25 days ago
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Job Description

ML Ops Developer

Experience

4 to 12 Years

Joining Location

Chennai

Interview Location

Chennai

Job Summary

We are seeking a highly skilled ML Ops Developer with strong expertise in building, deploying, monitoring, and managing Machine Learning solutions at scale. The ideal candidate should have hands-on experience in MLOps frameworks, cloud platforms, CI/CD pipelines, containerization, model monitoring, and automation of the end-to-end ML lifecycle.

Key Responsibilities

  • Design, develop, and maintain end-to-end MLOps pipelines across the Machine Learning lifecycle, including data preparation, model training, validation, deployment, monitoring, and retraining.
  • Implement CI/CD and Continuous Training (CT) pipelines for ML workflows with automated testing, model promotion, rollback strategies, and reproducible builds.
  • Build and manage scalable ML infrastructure using Docker, Kubernetes, MLflow, Kubeflow, or equivalent MLOps platforms.
  • Develop and support model serving and deployment frameworks for batch, real-time, and streaming workloads.
  • Establish monitoring and observability solutions for ML systems, including model performance, feature drift, concept drift, data quality, and operational health.
  • Configure alerting mechanisms and perform root cause analysis for production ML issues.
  • Deploy and manage ML workloads on cloud platforms such as AWS, Azure, or GCP using cloud-native services.
  • Implement security, governance, access controls, audit logging, and compliance standards for enterprise-grade ML platforms.
  • Collaborate with Data Scientists, Data Engineers, Platform Engineers, and Business teams to operationalize Machine Learning solutions.
  • Drive best practices for version control, model lifecycle management, infrastructure automation, scalability, and reliability.

More Info

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Key Skills

audit logging

automation of the end-to-end ML lifecycle

MLflow

model promotion

access controls

monitoring and observability solutions

Kubeflow

compliance standards

rollback strategies

cloud platforms

alerting mechanisms

reproducible builds

deployment frameworks

CI CD pipelines

MLOps frameworks

model serving