Edge AI Intern

2 weeks ago

Bengaluru, Karnataka, India QpiAI Full time
Edge AI Intern – Intelligence at the Edge | QpiAI
The Artificial Intelligence team at QpiAI is building the next frontier of AI-driven edge intelligence —powering smart devices, real-time perception systems, and decentralized learning pipelines for industry-grade applications.
From Agentic AI to Computer Vision and model optimization for edge hardware , we are designing cutting-edge solutions to push AI inference beyond the cloud. If you're excited by the idea of bringing Large Models to Tiny Devices, we want to hear from you.
Who We're Looking For
We're hiring Edge AI Interns to work alongside our product and engineering teams. You will be contributing to:
Edge AI model optimization, quantization, and ONNX conversion
Edge deployment workflows (e.g., Android/Linux) using Docker and Kubernetes
Real-time inference for CV and NLP applications on resource-constrained devices
Prototyping and testing model performance across ARM/NPU/EdgeTPU platforms
Integrating with QpiAI Pro and Agent Hive pipelines for edge-to-cloud AI orchestration
Ideal Candidate Traits
First principles thinker with a passion for AI at the edge
Strong problem-solving skills and curiosity for optimization trade-offs
Hands-on, self-motivated, and eager to experiment and iterate
Key Requirements
Solid Python skills with deep learning libraries (PyTorch, TensorFlow, ONNX, TorchVision, OpenCV)
Familiarity with model quantization, pruning, and export formats (TorchScript, ONNX)
Deep understanding of edge computing environments (e.g.,Jetson, ARM, Raspberry Pi)
Experience with Docker, Kubernetes, Linux terminal
Exposure to MLOps tools and model orchestration frameworks
Strong fundamentals in ML, DL, CNNs, and Transformer architectures
Understanding of Version Control Systems, Workflow Orchestration, CI/CD pipeline
Recent graduate or student in Computer Science, ECE, or AI-focused programs (IITs/NITs preferred)
Bonus Skills (Good to Have)
Knowledge of low-level optimizations (TVM, TensorRT, TFLite, OpenVINO)
Familiarity with MQTT, MinIO, or similar edge communication protocols
Experience with edge inference on audio, video, or multi-modal datasets
Exposure to LangGraph, LangFlow, or multi-agent frameworks

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