AI Intern – Computer Vision

Smartan

Chennai

Sur place

INR 223 200 - 446 400

Plein temps

14 jours+
Générateur de candidature

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Avantages offerts par ce poste

Mentorship from AI engineers
Exposure to real datasets and users
Opportunity for pre-placement offer based on performance

Résumé du poste

A technology firm in Chennai is looking for an AI Intern. In this hands-on role, you’ll work with senior engineers on developing deep learning models for video-based human activity analysis. Key responsibilities include designing and training models, building real-time computer vision pipelines in Python, and working with video data. You should have proficiency in Python, as well as experience with tools like PyTorch and TensorFlow. This internship also provides mentorship and potential pre-placement offers based on performance.

Qualifications

  • Proficiency in Python with understanding of performance optimization.
  • Experience with video-based models like SlowFast or I3D.
  • Familiarity with image/video preprocessing using NumPy.

Responsabilités

  • Design and fine-tune deep learning models for motion understanding.
  • Build real-time computer vision pipelines in Python.
  • Integrate AI modules with REST APIs or cloud systems.

Connaissances

Python proficiency
Experience with PyTorch
Understanding of camera calibration
Hands-on experience with video-based models
Familiarity with OpenCV

Outils

PyTorch
TensorFlow
Mediapipe

Description du poste

As an AI Intern, you’ll work closely with senior AI engineers on developing and deploying state‑of‑the‑art deep learning models for video‑based human activity analysis, pose estimation, and motion understanding. This is a hands‑on role focused on building and integrating real‑world AI systems, not academic research.

You’ll get the chance to work on projects involving posture correction, rep counting, and exercise classification, using tools and frameworks like YOLOv8, Vision Transformers, MMAction2, and Mediapipe.

Key Responsibilities
  • Design, train, test, and fine‑tune deep learning models for human motion understanding, classification, and detection using video data.
  • Apply transfer learning and fine‑tuning on state‑of‑the‑art (SOTA) models like YOLO, ResNet, EfficientNet, Vision Transformers, and UNet.
  • Build and optimize real‑time computer vision pipelines in Python using PyTorch and/or TensorFlow.
  • Implement pose estimation, skeleton tracking, and posture correction models using libraries such as Mediapipe or OpenPose.
  • Apply camera calibration techniques and geometric corrections for accurate visual measurements.
  • Preprocess large‑scale video datasets, augment data for robustness, and manage annotation workflows.
  • Integrate AI modules with REST APIs or cloud systems (optional), and assist in model packaging using ONNX, TensorRT, or TorchScript.
  • Collaborate with mentors and team members to align design, timelines, and delivery.
Required Skills

Proficiency in Python, with clean, modular code and understanding of performance optimization.

  • Experience with PyTorch and/or TensorFlow.
  • Hands‑on experience working with video‑based models (SlowFast, I3D, TimeSformer).
  • Strong grasp of camera calibration, 3D geometry, and motion analysis.
  • Familiarity with OpenCV, NumPy, and image/video preprocessing.
  • Comfortable integrating and adapting open‑source models and APIs.
Preferred Skills
  • Exposure to tools like MMAction2, Detectron2, or DeepSort.
  • Understanding of human activity recognition, especially in fitness or healthcare contexts.
  • Experience deploying models using ONNX, TensorRT, or similar inference engines.
  • Familiarity with cloud services (Azure, AWS) and model integration through REST APIs.
  • Knowledge of real‑time edge inference or embedded systems.
What You’ll Gain
  • Mentorship from seasoned AI engineers in a high‑impact production environment.
  • Experience working on end‑to‑end AI pipelines in a fast‑growing sector.
  • Exposure to real datasets, users, and performance‑critical deployment.
  • Opportunity to receive a pre‑placement offer (PPO) based on internship performance.
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