Technical Manager/Lead - Edge AI, DL & Computer Vision

Job description

**Role & responsibilities** Technical Delivery & Architecture - Lead end-to-end technical delivery of Edge AI, Computer Vision, and Generative / Agentic AI solutions - Drive architecture and delivery planning for AI systems deployed on edge devices and on-premise infrastructure - Guide implementation of: - Computer Vision and image processing pipelines - Deep Learning models optimized for edge inference - Generative AI and agent-based workflows for local reasoning, decision-making, and automation - Sensor integration and real-time data acquisition - Collaborate with architects to design hybrid AI systems where GenAI/agents operate locally or in coordination with centralized services Edge Optimization & Performance - Ensure optimization across: - Model size, inference latency, throughput, and accuracy trade-offs - Efficient execution of GenAI models and agent logic on constrained platforms - Platform-specific acceleration (CPU, GPU, NPU, DSP) - Drive techniques such as quantization, pruning, distillation, and hardware-aware optimization - Oversee benchmarking, performance tuning, and real-time validation Platform, Deployment & Lifecycle - Lead deployment of AI solutions on embedded, edge, and on-premise platforms - Coordinate integration with cameras, sensors, industrial devices, and IoT systems Domain & Stakeholder Collaboration - Translate domain needs into technical delivery plans, AI KPIs, and system constraints - Communicate risks, trade-offs, and performance expectations to stakeholders Delivery Leadership & Governance - Own delivery plans, schedules, dependencies, and technical risks for complex AI programs - Drive Agile / Hybrid delivery models supporting hardwaresoftware co-development - Ensure compliance with safety, regulatory, and quality standards where applicable **Preferred candidate profile** Edge AI & Deep Learning Expertise - Computer Vision: Deep expertise in CNNs, object detection (YOLO, SSD, Faster R-CNN), semantic segmentation (U-Net, DeepLab), instance segmentation (Mask R-CNN), and vision transformers - Model Optimization: Hands-on experience with quantization (INT8, FP16), pruning, knowledge distillation, and neural architecture search - Edge Frameworks: Proficiency with TensorFlow Lite, ONNX Runtime, OpenVINO, TensorRT, PyTorch Mobile, TVM, or similar - Hardware Platforms: Experience with NVIDIA Jetson (Nano, TX2, Xavier, Orin), Intel Movidius/NCS, Raspberry Pi, ARM Mali, Qualcomm NPUs - Image Processing: Strong foundation in OpenCV, PIL/Pillow, scikit-image, traditional CV algorithms, and image enhancement techniques - Generative AI: Knowledge of GANs, diffusion models, and VAEs for synthetic data generation and edge-based generation - Agentic AI: Understanding of reinforcement learning, decision-making systems, and autonomous agents for edge environments Domain Knowledge - Manufacturing: Understanding of industrial automation, machine vision systems, quality control processes, and factory standards (ISO 9001) - Medical Diagnostics: Familiarity with medical imaging modalities (X-ray, CT, MRI, ultrasound), DICOM standards, FDA regulatory pathways, and clinical workflows - Automotive: Knowledge of ADAS systems, autonomous driving stacks, automotive sensors (cameras, LiDAR, radar), and functional safety (ISO 26262) - Experience in at least one vertical with deep understanding of industry requirements and use cases Technical Infrastructure - Embedded Systems: Understanding of embedded Linux, RTOS, device drivers, and low-level optimization - Sensor Integration: Experience with camera interfaces (CSI, USB, MIPI), sensor protocols (I2C, SPI, CAN), and multi-sensor fusion - Edge Computing: Knowledge of edge-cloud architectures, fog computing, and distributed inference strategies - MLOps for Edge: Experience with edge-specific CI/CD, containerization (Docker on edge), and OTA update mechanisms - Programming: Proficiency in Python, C/C++ for performance optimization, and CUDA/OpenCL for GPU acceleration \\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_ Preferred Qualifications - Bachelor's degree in Computer Science, Software Engineering, or related technical field; Master's degree preferred - Exposure to embedded AI accelerators and edge platforms - Familiarity with safety-critical or regulated environments