{"id":2944722,"url":"https://alion.io/job/buspatrol-embedded-ai-engineer","title":"Embedded AI Engineer","company":{"id":3767,"name":"BusPatrol","domain":"buspatrol.com","url":"https://alion.io/company/buspatrol","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":61,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CNN","optional":false},{"name":"Computer Vision","optional":false},{"name":"Edge AI","optional":false},{"name":"FFmpeg","optional":false},{"name":"GStreamer","optional":false},{"name":"LiteRT","optional":false},{"name":"OCR","optional":false},{"name":"ONNX","optional":false},{"name":"OpenCV","optional":false},{"name":"OpenVINO","optional":false},{"name":"Quantization","optional":false},{"name":"TensorRT","optional":false},{"name":"TFLite","optional":false},{"name":"YOLO","optional":false},{"name":"C","optional":true},{"name":"C++","optional":true},{"name":"CMake","optional":true},{"name":"gRPC","optional":true},{"name":"Linux","optional":true},{"name":"MQTT","optional":true},{"name":"Python","optional":true},{"name":"TensorFlow","optional":true},{"name":"ZeroMQ","optional":true}],"status":"live","first_seen_at":"2026-10-20T14:54:34Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-08T09:35:59Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":18},"description":"At BusPatrol, we’re transforming how communities protect students and enforce traffic safety-through intelligent, real-time video analytics and automated enforcement. As part of this mission, we are advancing the shift from cloud-dependent AI to intelligent edge computing.\nWe’re looking for an Embedded AI Engineer to lead the development and deployment of deep learning models on IoT and edge computing platforms. You’ll work on converting and optimizing computer vision models-such as CNNs, YOLO, RNNs, and OCR-for execution on high-efficiency hardware accelerators like Hailo, NVIDIA Jetson, Intel Movidius, or ARM-based NPUs.\nThis role is central to scaling BusPatrol’s next-generation Edge AI platform, enabling real-time safety decisions and unlocking new capabilities for school bus fleets, student safety, and public transportation environments.\nKey Responsibilities\nDevelop and optimize embedded software and AI model inference pipelines for real-time object detection, tracking, and classification at the edge.\n\nDeploy and maintain vision-based deep learning models (YOLO, CNN, RNN, OCR) on resource-constrained devices using AI accelerators (e.g., Hailo, Jetson, Coral, ARM).\n\nLead cloud-to-edge transformation of AI models, including quantization, pruning, conversion, and hardware-aware optimization.\n\nCollaborate with AI/ML researchers to integrate trained models into embedded applications and validate edge inference accuracy.\n\nOptimize for performance, latency, and power efficiency in real-world mobile or vehicle-mounted environments.\n\nWork with hardware abstraction layers, toolchains, and SDKs for AI acceleration (e.g., HailoRT, TensorRT, OpenVINO, ONNX, TFLite).\n\nDesign and implement robust, fault-tolerant inference pipelines using tools like GStreamer, OpenCV, or FFmpeg.\n\nContribute to system architecture discussions focused on scalable AI at the edge, real-time event capture, and safety-critical decision support.\n\nPreferred Qualifications\nProficient in C/C++ and Python for embedded development and AI integration.\n\nExperience with quantization, pruning, and model conversion for edge inference (ONNX, TFLite, TensorRT, Dataflow Compiler etc.).\n\nHands-on experience with at least one AI hardware platform (e.g., Hailo, NVIDIA Jetson, Intel Movidius, ARM Ethos-U, Google Coral).\n\nSolid understanding of deep learning architectures and model behavior (CNN, YOLOv5/v8, OCR, ReID, RNNs).\n\nStrong knowledge of video/image processing pipelines using GStreamer, OpenCV, or similar.\n\nExperience working with Linux-based embedded systems, build systems (Yocto, CMake), and cross-compilation toolchains.\n\nUnderstanding of IoT/vehicle edge environments, including system resource constraints, fault tolerance, and field deployment challenges.\n\nBachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, AI, or a related technical field.\n\nNice to Have\nFamiliarity with multi-camera systems, object re-identification, or behavior/event detection at the edge.\n\nKnowledge of real-time messaging and telemetry systems (MQTT, ZeroMQ, or gRPC).\n\nExperience in public safety, ADAS, surveillance, or other vision-based safety-critical applications.\n\nFamiliarity with data privacy, security, and compliance concerns for edge AI systems.\n\nWhy Join Us\nBe a core driver in bringing safety AI to the edge, where milliseconds matter.\n\nHelp scale an intelligent, distributed AI platform that makes real-world impact across thousands of vehicles.\n\nWork at the intersection of AI, embedded systems, and public safety in a fast-paced, purpose-driven environment.\n\nCollaborate with engineers, data scientists, product managers, and field ops to build AI that directly saves lives.","description_format":"text","description_chars":3701,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Public Transport","Law Enforcement","Computer Vision","Video 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