About the role

Job Description

AI/ML Engineer – Computer Vision

1. Key Responsibilities

Dataset & Annotation

Define annotation standards (bounding boxes, oriented bounding boxes, polygons, instance masks) based on the problem statement.
Ensure high-quality annotations, dataset consistency, class balance, and adequate edge-case coverage.
Manage dataset curation, validation, augmentation, and versioning for model training.

Model Development & Training

Benchmark and evaluate different model architectures (YOLOv8/YOLOv11, YOLOv11-OBB, Instance Segmentation, Vision Transformers, etc.) based on accuracy, latency, and deployment requirements.
Train, fine-tune, and optimize deep learning models using transfer learning, data augmentation, hyperparameter tuning, and hard-negative mining.
Continuously improve model performance through experimentation and evaluation.

Computer Vision Development

Develop image and video analytics pipelines using OpenCV and modern Computer Vision techniques.
Implement object detection, object tracking, image segmentation, OCR, and feature extraction solutions as required.
Develop robust preprocessing and post-processing pipelines for production-ready inference.

Model Deployment

Export trained models to ONNX and optimize them for deployment using TensorRT, ONNX Runtime, or equivalent inference frameworks.
Perform model optimization through quantization (INT8/PTQ), profiling, and performance tuning on GPU or edge devices.
Develop scalable inference pipelines, including camera integration, preprocessing, inference, post-processing, and result generation.

Model Evaluation & MLOps

Define and monitor model performance metrics such as mAP, Precision, Recall, F1-Score, FPS, and inference latency.
Maintain reproducible training pipelines, dataset versioning, and model versioning.
Support continuous model improvement through retraining and performance monitoring.

2. Mandatory Skills

Area	Requirement
Programming	Strong proficiency in Python. Working knowledge of C++ for inference-side integration is preferred.
Deep Learning Frameworks	Hands-on experience with PyTorch. Experience with the Ultralytics YOLO ecosystem (training, fine-tuning, export, custom datasets).
Computer Vision	Strong understanding of Object Detection, Image Classification, Segmentation, Object Tracking, and Video Analytics.
YOLO	Practical experience with YOLO models (preferably YOLOv11), including training, evaluation, deployment, confidence threshold tuning, IoU, NMS, and performance optimization.
OpenCV	Strong experience with OpenCV for image processing, geometric transformations, camera integration, feature extraction, and video processing.
Model Deployment	Experience deploying AI models using ONNX, ONNX Runtime, TensorRT, or similar inference frameworks on GPU or edge devices.
Model Optimization	Hands-on experience with quantization, inference optimization, and performance tuning for production deployments.
Data Pipeline	Experience with annotation tools such as CVAT, Roboflow, or Label Studio. Knowledge of dataset preparation, augmentation, and data quality management.
Engineering	Git, Docker, Linux, virtual environments, modular coding practices, and API integration.
Evaluation	Strong understanding of model evaluation metrics including mAP, Precision, Recall, F1-Score, FPS, latency, and confusion matrix analysis.
Communication	Ability to explain technical decisions, model performance, and trade-offs to technical and non-technical stakeholders.

3. Good to Have

Area	Nice to Have
Object Tracking	Experience with ByteTrack, DeepSORT, BoT-SORT, or similar multi-object tracking algorithms.
Oriented Detection	Experience with YOLO-OBB, rotated bounding boxes, angle regression, and rotated-NMS.
Segmentation	Experience with Instance Segmentation models such as YOLO-Seg, Mask R-CNN, or SAM.
OCR	Experience with PaddleOCR, EasyOCR, Tesseract, or TrOCR.
Vision Models	Familiarity with Vision Transformers (ViT), CLIP, DINO, Grounding DINO, Florence-2, or similar modern vision models.
Inference Runtime	Experience with TensorRT, OpenVINO, ONNX Runtime, TFLite, or NVIDIA DeepStream.
MLOps	Experience with MLflow, Weights & Biases, DVC, model versioning, and automated training pipelines.
Cloud & Deployment	Exposure to AWS, Azure, GCP, Docker, Kubernetes, and scalable AI deployment.
GPU Optimization	Knowledge of CUDA, NVIDIA GPU optimization, mixed precision inference, and performance profiling.

AI/ML - Computer Vision

About the role