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TinyML / Embedded AI Principal Engineer

TinyML / Embedded AI Principal Engineer

L&T Technology Servicesbangalore, karnataka, in
2 days ago
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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