AI Engineer

Job Title: AI Engineer

Company: Edgecore Networks

Location: North Bangalore Cluster (Near Hebbal)

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

Contact Details:

Email: [HIDDEN TEXT]

Email: [HIDDEN TEXT]

Skills Required
Performance Tuning, Tensorflow, Pytorch, Embedded Systems, Hardware Integration