Embedded systems architect – edge ai Jobs – 157 Open Jobs Today

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Technical Project Delivery Manager – Edge AI & Computer Vision

Tata Elxsi -
Bengaluru, Karnataka

Apply Now

Job details

Qualifications

BCS
TensorFlow
CI/CD
ADAS
Image processing
Computer Science
Software Engineering
Computer vision
Software deployment
R
ISO 9001
Master's degree
C++
Docker
C
Bachelor's degree
Quality control
Continuous integration
Deep learning
OpenCV
X-ray
Agile
Linux
ARM
Manufacturing
Medical imaging
AI
Python
FDA regulations
Raspberry Pi

Full job description

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

Apply Now

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embedded systems architect – edge ai jobs

Technical Project Delivery Manager – Edge AI & Computer Vision

Senior Engineering Manager - GenAI

Principal Engineer, Radio Systems

Senior Embedded Systems Architect

Member of Technical Staff- BSP

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Engineer – AI Quality Assurance (QA)

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Firmware Engineer (TPM)

Tech Lead – MASTRO

DataScience Manager Knowledge Management

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