As a Computer Vision Engineer – Developer, you will contribute to the development and maintenance of AI pipelines addressing key problems such as object detection, item tracking, recognition, and counting.

Working under the guidance of senior engineers and data scientists, you’ll assist in implementing deep learning models, preparing datasets, and validating performance metrics in real-world scenarios.

This is an ideal role for early-career AI professionals who are passionate about CV and eager to grow through exposure to synthetic data, foundational models, and production-grade systems.

Assist in building and testing CV models for detection, tracking, classification tasks and latest foundation and VLM models.

Prepare and annotate image/video datasets, support data ingestion and cleaning pipelines

Contribute to writing and debugging training scripts, model loaders, and preprocessing functions

Run evaluation jobs and generate performance reports using tools like TensorBoard or custom scripts

Support error analysis by identifying model weaknesses across edge cases

Collaborate with senior engineers on integrating models into scalable inference pipelines

Help visualize model outputs, draw bounding boxes, heatmaps, or segmentation masks for explainability

Document experiments and code for reproducibility and knowledge sharing

"Used OpenCV, MediaPipe, and scikit-image for preprocessing, motion analysis, and visual overlays

Integrated DL models with post-processing logic (e.g., NMS, temporal smoothing, event triggering)

Ensured low-latency inference by profiling and tuning frame-wise preprocessing

Supported integration of RTSP video feeds and video decoders in test pipelines

point-cloud ingestion and processing (Open3D/PCL), calibration/registration (ICP/FGR), 3D detection/segmentation with sparse CNNs/PointNet, and RGB–LiDAR–IMU fusion."

Good Python skills and working knowledge of PyTorch or TensorFlow

Familiarity with image processing libraries (e.g., OpenCV, PIL) and dataset tools (e.g., COCO format, YOLO datasets)

Eagerness to learn, take feedback, and contribute in collaborative development environments

Exposure to object detection/tracking projects (academic, hackathons, or prior work)

Basic understanding of synthetic data or 3D asset usage in training pipelines

Familiarity with Git, Linux command line, and Jupyter Notebooks

Interest in building a career in computer vision and AI for real-world automation

Bachelor’s degree in Computer Science, Data Science, or a related technical field

1–3 years of experience or strong internship/projects in computer vision or ML model development