We are seeking a highly technical Project Delivery Manager to lead the development and deployment of Edge AI and on-premises AI solutions using Deep Learning, Computer Vision, and Generative / Agentic AI.
Key Responsibility:
- 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-premises 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
- 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
- Lead deployment of AI solutions on embedded, edge, and on-premise platforms
- Coordinate integration with cameras, sensors, industrial devices, and IoT systems
- Translate domain needs into technical delivery plans, AI KPIs, and system constraints
- Communicate risks, trade-offs, and performance expectations to stakeholders
- Own delivery plans, schedules, dependencies, and technical risks for complex AI programs
- Drive Agile / Hybrid delivery models supporting hardware–software co-development
- Ensure compliance with safety, regulatory, and quality standards where applicable
Required Technical Skills & Experience:
- 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
- Edge Deployment: Extensive experience deploying models on resource-constrained devices with <4GB RAM, <10W power budgets
- 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
- 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
- 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
