Embedded AI Engineer

BusPatrol

. Develop and optimize embedded software and AI model inference pipelines for real-time object detection, tracking, and classification at the edge .

Posted 11/6/2026full-timeHyderabad • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing embedded software and AI model inference pipelines for real-time applications, with a strong focus on performance, latency, and power efficiency in edge environments. Proficient in integrating deep learning models and collaborating with researchers to ensure accurate edge inference.

Highest-signal resume keywords

C/C++ ProgrammingPython DevelopmentDeep Learning Model OptimizationEmbedded Systems ExperienceVideo/Image Processing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Embedded Software DevelopmentAI Model InferenceQuantizationPruningModel ConversionDeep Learning ArchitecturesLinux-Based SystemsCross-Compilation ToolchainsFault ToleranceReal-Time Object Detection

Tools & Technologies

HailoRTTensorRTOpenVINOONNXTFLiteGStreamerOpenCVFFmpegYoctoCMake

Industry Keywords

AI AcceleratorsEdge AIIoTVehicle Edge EnvironmentsResource Constraints

Tech Stack

Tools & technologies

CloudFFmpegIoTLinuxPythonC++

About the role

Key responsibilities & impact

Develop and optimize embedded software and AI model inference pipelines for real-time object detection, tracking, and classification at the edge
Deploy and maintain vision-based deep learning models on resource-constrained devices using AI accelerators
Lead cloud-to-edge transformation of AI models through quantization, pruning, conversion, and hardware-aware optimization
Collaborate with AI/ML researchers to integrate trained models into embedded applications and validate edge inference accuracy
Optimize performance, latency, and power efficiency in mobile or vehicle-mounted environments
Work with hardware abstraction layers, toolchains, and AI acceleration SDKs including HailoRT, TensorRT, OpenVINO, ONNX, and TFLite
Design and implement robust, fault-tolerant inference pipelines using GStreamer, OpenCV, or FFmpeg
Contribute to architecture discussions involving scalable edge AI, real-time event capture, and safety-critical decision support

Requirements

What you’ll need

Proficient in C/C++ and Python for embedded development and AI integration
Experience with quantization, pruning, and model conversion for edge inference using ONNX, TFLite, TensorRT, Dataflow Compiler, or similar tools
Hands-on experience with at least one AI hardware platform, such as Hailo, NVIDIA Jetson, Intel Movidius, ARM Ethos-U, or Google Coral
Solid understanding of deep learning architectures and model behavior, including CNN, YOLOv5/v8, OCR, ReID, and RNNs
Strong knowledge of video/image processing pipelines using GStreamer, OpenCV, or similar tools
Experience with Linux-based embedded systems, build systems such as Yocto and CMake, and cross-compilation toolchains
Understanding of IoT/vehicle edge environments, system resource constraints, fault tolerance, and field deployment challenges
Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, AI, or a related technical field

Benefits

Comp & perks

Hybrid work arrangement
Opportunity to work on safety AI, embedded systems, and public safety
Collaboration with engineers, data scientists, product managers, and field operations
Fast-paced, purpose-driven work environment
Opportunity to build AI systems deployed across thousands of vehicles