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

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

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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