Applied Computer Vision Engineer; Deep Learning

Position: Applied Computer Vision Engineer (Deep Learning)
Important - ANSWER 3 SCREENING QUESTIONS MENTIONED AT THE END OF THIS JOB POST.  No-Answers = No-Selection

Role Overview

We are building advanced  AI-driven image analysis systems  that extract structured geometric features from complex visual data. The role involves developing and deploying deep learning models for  object detection, segmentation, and precise feature extraction  from high-resolution images.

The ideal candidate will have strong experience in  computer vision, deep learning model training, and image processing pipelines , with the ability to take models from  research experimentation to production deployment .

Key Responsibilities

Design and develop deep learning models for image understanding tasks such as:
object detection
semantic and instance segmentation
feature extraction and geometric analysis

Build and train models using modern deep learning frameworks such as  PyTorch .
Develop detection pipelines using architectures like  YOLO or similar real-time detection models .
Implement segmentation pipelines using models such as  SAM, Mask R-CNN, Detectron2, or equivalent frameworks .

Design and implement  image preprocessing and augmentation strategies  to improve model robustness.
Develop  post-processing algorithms  to extract precise coordinates and vectorized representations of visual features.

Create and maintain  training pipelines, evaluation metrics, and dataset management workflows .
Work with annotation tools and create scalable labeling strategies for large image datasets.
Optimize model performance for  GPU training and efficient inference .
Collaborate with engineering teams to integrate models into production environments.

Required Skills  Strong programming skills in  Python .
Hands-on experience with  PyTorch or similar deep learning frameworks .
Experience training and fine-tuning  object detection models (YOLO, Faster R-CNN, etc.) .

Experience with  image segmentation models  such as Mask R-CNN, SAM, or similar.
Strong understanding of  computer vision fundamentals .

Experience with  OpenCV  and image processing techniques.

Experience with  data augmentation libraries  such as Albumentations or torch vision.
Experience working with  annotation tools  like CVAT, Label Studio, or Roboflow.
Experience training models on  GPU environments (CUDA) .
Familiarity with  Docker and containerized ML environments .

Preferred Qualifications  Experience building  end-to-end computer vision pipelines .

Experience with  geometric feature extraction or curve detection  in images.

Experience with  skeletonization, edge detection, or contour analysis .

Experience with  experiment tracking tools (Weights & Biases, MLflow) .
Experience deploying models using  Torch Serve, Triton, or similar inference frameworks .
Strong Git Hub portfolio demonstrating real computer vision projects.

Experience Level  3–5 years experience in  Computer Vision / Deep Learning
Candidates with  strong personal or open-source projects  are highly encouraged to apply.

Nice-to-Have

Experience with :
Detectron2
segmentation pipelines
model optimization and quantization
large-scale image dataset handling

What We Offer  Opportunity to work on  cutting-edge applied computer vision problems
Access to  large real-world image datasets
High-performance GPU training environments
Ability to build models that move  from research to real production systems

Ideal Candidate Profile We are looking for engineers who enjoy:
solving challenging visual perception problems
building practical AI systems
experimenting with new deep learning architectures
taking ownership of model development and deployment

Screening Questions

Question 1:  - Describe a computer vision model you have personally trained from scratch (not just fine-tuned).
Explain:
the dataset size and source
how the data was annotated
which model architecture you used
what problems you faced during training
how you improved the model performance

Question 2:

-  You are given  10,000 images  but only  800 are labeled  for a segmentation task.
Explain how you would train a model effectively using this dataset. Describe the strategy you would use to improve performance.

Question 3:

-  You have trained a PyTorch computer vision model that works well in experiments.
How would you deploy it so that a production system can process images in real time?
Explain the  tools and architecture you would use.

You can also send the responses to the above questions with your contact details on