▷ [Urgent] TinyML / Embedded AI Principal Engineer

16 hours ago

Bengaluru India L&T Technology Services Full time

Job Description Position Title: TinyML / Embedded AI Principal Engineer Location: Bangalore Experience Required: 15+ Years Employment Type: Full-Time Role Purpose: The TinyML / Embedded AI Principal Engineer will lead the design, development, optimization, and deployment of AI solutions on edge and resource-constrained embedded devices. This role is critical to enabling real-time, mission-critical AI inference across advanced programs, with a focus on sensor fusion, hardware-software co-design, and embedded intelligence under strict latency, power, and memory constraints. Key Responsibilities: - Architect and deploy TinyML / Embedded AI models for real-time inference on microcontrollers, SoCs, FPGAs, and custom accelerators. - Optimize AI models using quantization, pruning, and compression for performance and efficiency. - Select and integrate embedded AI hardware platforms (e.g., NVIDIA Jetson, ARM Ethos-U, Kendryte, FPGA, ASIC). - Develop real-time computer vision and multi-sensor fusion algorithms (video, radar, LiDAR, IMU). - Implement robust object detection, classification, and tracking under challenging conditions. - Integrate AI into embedded/mechatronic systems ensuring reliability, scalability, and security. - Lead HIL simulations, lab validation, and field testing of embedded AI systems. - Mentor junior engineers and drive capability growth across the organization. - Serve as technical authority on embedded AI deployment and optimization strategies. - Produce technical documentation, risk assessments, and stakeholder reports. - Research and adopt emerging TinyML frameworks (TensorFlow Lite Micro, Edge Impulse, PyTorch Mobile). - Collaborate with cross-functional teams to design next-gen AI-enabled embedded architectures. - Generate risk/progress reports and propose mitigation strategies. Experience & Skills: - 15+ years of experience in AI development for embedded or defense systems. - Proven expertise in edge AI optimization (quantization, pruning, compression). - Hands-on experience with AI hardware toolchains (TensorRT, ARM CMSIS-NN, OpenVINO, Vitis AI). - Exposure to NLP, robotics, predictive analytics, and autonomous systems is a plus. - Advanced programming and software engineering skills. - Deep understanding of edge computing and computational power optimization. Qualifications: - Master's or PhD in Computer Science, Electrical Engineering, or related field. - Certifications in TinyML, embedded systems, or hardware acceleration are a plus. - Additional training in AI-related technologies is an advantage.

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