Overview
What you ll do:
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Lead ML model lifecycle, from research and experiments to implementation and deployment.
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Build and deploy deep learning models on GCP and edge devices , ensuring real-time inference.
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Combine multiple sensor inputs into powerful multi-modal ML models.
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Implement and refine CNNs, Vision Transformers(ViT) , and other architectures.
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Design sensor-fusion methods for better perception and decision-making.
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Optimize inference for low-latency , efficient production use.
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Work closely with software and hardware teams to bring AI into mission-critical systems .
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Create and scale pipelines for training, validating, and improving models .
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Ensure your models are robust, interpretable, and secure.
What you ll bring:
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Deep expertise in TensorFlow and PyTorch .
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Hands-on experience with CNNs, ViTs, and DL architectures.
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Experience with multi-modal ML and sensor fusion .
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Cloud deployment skills- GCP preferred .
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Edge AI know-how (NVIDIA Jetson, TensorRT, OpenVINO).
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Proficiency in quantization, pruning, and real-time model optimization .
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Solid computer vision and object detection experience.
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Ability to work with limited datasets (using VAEs or similar) to generate synthetic data , and experience with annotation and augmentation.
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Strong coding skills in Python and C++ , with high-performance computing expertise.
Nice to have:
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2-6 years of relevant experience.
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MLOps experience- CI/CD, model versioning, monitoring.
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Knowledge of reinforcement learning.
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Experience in working in fast-paced startup environments.
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Experience in AI for autonomy, robotics, or UAV systems.
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Knowledge of embedded systems and hardware acceleration for AI.