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Tier-1 brand plus metro location increases visibility, while niche edge specialization moderates applicant density.
Edge hardware, computer vision, and MLOps specialization substantially reduce cross-industry transferability.
Multiple mandatory technical skills and edge deployment experience create stringent shortlisting filters.
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Design, develop, and optimize computer vision or sensor-based AI models for deployment on edge devices with low power and low latency constraints.
Deploy, monitor, and maintain AI models on edge platforms like NVIDIA Jetson and Azure IoT Edge, ensuring efficient inference through model compression and quantization.
Build and maintain scalable MLOps pipelines integrating cloud-trained AI models with edge deployments using tools such as Databricks, MLflow, and Azure Machine Learning.
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
Proven experience developing and deploying machine vision or sensing modality AI models using PyTorch, TensorFlow, or similar frameworks.
Hands-on experience with edge device deployment platforms such as NVIDIA Jetson, Raspberry Pi, or Intel OpenVINO.
Experience with MLOps tools (Databricks, MLflow, Kubeflow, or Azure Machine Learning) and proficiency in Python, containerization (Docker), and CI/CD pipelines.
Experienced in end-to-end AI model lifecycle on edge hardware balancing resource constraints and performance.
Strong collaborator capable of integrating AI solutions with cross-functional teams including data engineers and product developers.
Technically proficient in MLOps and edge AI engineering environments, with familiarity in AI model optimization and deployment workflows.