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Deep Learning & Computer Vision Engineer

5 years

0 Lacs

new delhi delhi india

Posted:1 week ago| Platform: Linkedin logo

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Skills Required

learning vision automation ai creativity technology cutting design research segmentation aws gcp preprocessing data model support tensorflow pytorch docker git engineering robotics optimization quantization

Work Mode

On-site

Job Type

Full Time

Job Description

Job Title:

Company:

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About Us

automation, AI, and computer vision


Role Overview

Deep Learning & Computer Vision Engineer


Key Responsibilities

  • Design and implement

    deep learning models

    for image classification, object detection, segmentation, and tracking.
  • Develop

    computer vision pipelines

    for real-time applications.
  • Research and apply the latest

    AI and computer vision techniques

    from research papers to production systems.
  • Deploy, optimize, and maintain models on

    cloud platforms

    (AWS, GCP, Azure).
  • Work with large datasets, performing preprocessing and

    data augmentation

    .
  • Collaborate with cross-functional teams to integrate AI into products.
  • Monitor model performance and improve efficiency.

  • Mentor junior engineers

    and support team knowledge sharing.


Required Skills & Qualifications

  • 3–5 years of experience in

    deep learning and computer vision

    .
  • Proficiency in

    Python

    and frameworks like

    TensorFlow, PyTorch, Keras

    .
  • Strong understanding of

    OpenCV

    and computer vision algorithms.
  • Hands-on experience with neural architectures (

    CNNs, YOLO, ResNet, Transformers

    ).
  • Experience deploying models on

    AWS/GCP/Azure

    and knowledge of

    MLOps

    (Docker, Git, CI/CD).
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.


Preferred Qualifications

  • Experience with

    ROS

    and robotics applications.
  • Knowledge of

    model optimization techniques

    (quantization, pruning).
  • Familiarity with

    edge deployment

    (TensorFlow Lite, ONNX).
  • Startup experience and ability to thrive in a dynamic environment.
  • Contributions to

    open-source projects

    or published research.

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