AI Engineer

Edgecore Networks Corporation; Bengaluru; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Edgecore Networks Corporation seeks dynamic AI Engineers to design, develop, and deploy advanced AI/ML solutions for edge devices. The role focuses on LLMs, vision transformers, and embedded AI optimization, with deployment on Qualcomm Linux, Yocto, and similar platforms.

The ideal candidate will have 5–10 years of hands-on experience with AI/ML, deep learning frameworks, and hardware integration, including performance tuning and inference acceleration on embedded hardware.

Qualifications

• Experience in designing, developing, and deploying AI/ML models for real-world use cases.
• Hands-on with deep learning frameworks such as PyTorch and TensorFlow.
• Knowledge of embedded systems and edge AI deployment.
• Familiarity with Linux-based systems and hardware integration.

Responsibilities

• Design, develop, and train AI models including LLMs, diffusion models, and vision transformers for edge deployment.
• Perform model optimization and quantization (LoRA, QLoRA, PEFT) and hyperparameter tuning.
• Build data pipelines, synthetic data generation, augmentation, and cleaning.
• Port and optimize AI models on embedded hardware using frameworks like TensorRT, TFLite, and Edge TPU.
• Develop end-to-end AI pipelines on embedded Linux platforms (Yocto, OpenWRT, or custom NOS).
• Implement inference acceleration, memory and power optimization for edge AI.
• Integrate AI models with sensors, cameras, actuators, and protocols (CAN, Modbus, MQTT, ROS2).
• Conduct hardware-in-the-loop testing and performance profiling.

Skills

AI/ML model development

Problem-solving

Analytical thinking

Embedded systems understanding

Tools

PyTorch

TensorFlow

TensorRT

NCNN

TFLite

Edge TPU

Yocto

Job description

Required Experience: 5 to 10 Years

Summary:

We are looking for dynamic and skilled AI Engineers to design, develop, and deploy advanced AI/ML solutions for real-world applications. The role involves working on cutting-edge technologies including large language models, computer vision, and embedded AI systems, with a strong focus on optimization and deployment on edge devices.

Key Responsibilities:

• Design, develop, and train custom AI models such as LLMs, diAusion models, and vision transformers for specific use cases.
• Perform model fine-tuning, quantization, distillation, and optimization using techniques like LoRA, QLoRA, and PEFT.
• Conduct hyperparameter tuning, ablation studies, and benchmarking to improve model performance.
• Build and manage large-scale data pipelines, including synthetic data generation, data augmentation, and data cleaning.
• Port, deploy, and optimize AI models on embedded hardware using frameworks such as TensorRT, NCNN, TFLite, and Edge TPU.
• Develop end-to-end AI pipelines on embedded Linux platforms (Qualcomm Linux, Yocto, OpenWRT, or custom NOS).
• Implement inference acceleration, memory optimization, and power-eAicient AI solutions.
• Integrate AI models with hardware components such as sensors, cameras, actuators, and communication protocols (CAN, Modbus, MQTT, ROS2).
• Perform hardware-in-the-loop testing, debugging, and performance profiling.

Required Skills & Qualifications:

• Strong experience in AI/ML model development and deployment
• Hands-on experience with deep learning frameworks (PyTorch, TensorFlow, etc.)
• Knowledge of embedded systems and edge AI deployment Experience with model optimization techniques and performance tuning
• Familiarity with Linux-based systems and hardware integration
• Strong analytical and problem-solving skills