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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
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
