The Role
The role involves operationalizing AI models by building MLOps/LLMOps pipelines, automating deployment, monitoring model performance, and ensuring governance and compliance.
Summary Generated by Built In
Responsible for operationalizing AI models, ensuring scalable, reliable, and automated deployment of ML and LLM solutions across environments.
Responsibilities: -
- Build and manage MLOps and LLMOps pipelines
- Automate model deployment using CI/CD pipelines
- Monitor model performance, drift, and retraining cycles
- Manage model serving frameworks (e.g., vLLM, TGI, Ray Serve)
- Implement experiment tracking and model versioning
- Ensure governance, reproducibility, and compliance
Requirements: -
Must have minimum 4 years of experience
Experience with Kubernetes, Docker
CI/CD tools (GitLab, Jenkins, Azure DevOps)
ML frameworks (TensorFlow, PyTorch)
Knowledge of LLM serving and optimization
Skills Required
- Minimum 4 years of experience
- Experience with Kubernetes and Docker
- Experience with CI/CD tools (GitLab, Jenkins, Azure DevOps)
- Experience with ML frameworks (TensorFlow, PyTorch)
- Knowledge of LLM serving and optimization
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The Company
