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Specialized ML/CV MLOps requirements narrow the candidate pool despite metro location and known services brand.
Role demands niche computer-vision and MLOps expertise, limiting transferability across non-AI industries.
Extensive mandatory MLOps, CV tooling, deployment, and observability skills create strict technical shortlisting filters.
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Build and maintain end-to-end ML pipelines for computer vision including data ingestion, preprocessing, training, evaluation, and inference automation.
Deploy and serve computer vision models using Docker, Kubernetes, and model-serving frameworks enabling real-time and batch inference.
Implement model monitoring and optimization techniques including performance monitoring, drift detection, quantization, pruning, and edge device deployment.
Hands-on experience operationalizing computer vision models and ML infrastructure including DevOps practices.
Experience with tools like MLflow, Kubeflow, DVC, Airflow, Docker, Kubernetes, and model-serving frameworks (TensorFlow Serving, TorchServe, Triton).
Bachelor's or Master's degree in Business Administration, Communication, Marketing, Sales, or related fields as specified.
Work Experience Required: Not explicitly mentioned in the JD.
Technical expertise in ML Ops specifically around computer vision model lifecycle and deployment in production environments.
Experience collaborating closely with data scientists and DevOps to integrate ML models into CI/CD and cloud-native architectures.
Familiarity with edge AI deployment and optimization frameworks for resource constrained devices is advantageous but optional.