TinyML / Embedded AI Principal Engineer

20 hours ago

bangalore, India L&T Technology Services Full time

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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