Key Responsibilities:

- Develop, train, and optimize production-grade computer vision models - object detection, instance segmentation, classification, and tracking.

- Deploy and optimize models on embedded AI platforms and edge devices for low-latency, real time inference.

- Build and integrate end-to-end CV pipelines into IoT systems and edge-to-cloud architectures.

- Apply model optimization techniques - quantization, pruning, distillation, and hardware acceleration - to meet edge constraints.

- Establish and maintain robust MLOps practices: CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelines.

- Benchmark and profile model performance (latency, throughput, memory, power) across target hardware.

- Collaborate with hardware, firmware, and product teams to ship reliable, scalable edge AI solutions.

- Maintain high standards of code quality, documentation, and reproducibility across the model lifecycle.

Required Skills & Experience:

- 3 - 8 years of hands-on experience in computer vision and deep learning.

- Strong proficiency in Python and CV / DL frameworks (PyTorch, TensorFlow, OpenCV).

- Proven track record building and deploying production-grade CV models.

- Deep expertise in instance segmentation and real-time CV systems.

- Hands-on embedded AI / edge deployment experience (e.g., NVIDIA Jetson, TensorRT, OpenVINO, Coral, ARM-based platforms).

- Experience with IoT architectures and edge-cloud integration.

- Strong MLOps skills: CI/CD, containerization (Docker), model registries, monitoring, and automated retraining.

- Solid understanding of model optimization for constrained hardware (quantization, pruning, compression).

Nice to Have:

- C++ for performance-critical inference paths.

- Familiarity with cloud ML workflows (AWS / Azure / GCP).

- Exposure to ONNX, GStreamer, or NVIDIA DeepStream pipelines.

- Knowledge of MQTT or other IoT messaging protocols.

- Experience with Kubernetes / Kubeflow for ML orchestration.

What We Offer:

- Opportunity to work on cutting-edge edge AI and real-time vision products.

- A collaborative, hands-on engineering culture based in Chennai.

- Ownership of the full model lifecycle, from research to production.