AI/Deep Learning Engineer

About the Role

We are looking for an AI/Computer Vision Engineer to build and optimize real-time video analytics solutions for SafeSwim.AI. The ideal candidate should have experience. Developing, training, and deploying deep learning models for object detection, tracking, and video understanding. You will work closely with product, cloud, and engineering teams to deliver scalable AI solutions for safety-critical environments.

Key Responsibilities

·       Develop, train, and optimize computer vision models for real-time object detection and tracking.

·       Work with YOLO, RT-DETR, or similar object detection frameworks.

·       Prepare, clean, and annotate datasets for model training.

·       Fine-tune pre-trained models and improve detection accuracy.

·       Evaluate model performance using precision, recall, mAP, and latency metrics.

·       Optimize inference for edge devices and GPU-based deployments.

·       Integrate AI models into production applications.

·       Work with video streams (RTSP, CCTV, IP Cameras).

·       Collaborate with software engineers for deployment and API integration.

·       Troubleshoot model performance and continuously improve detection accuracy.

·       Maintain documentation and experiment tracking.

Required Skills

Computer Vision

·       Object Detection

·       Image Classification

·       Object Tracking

·       Instance Segmentation (preferred)

·       Video Analytics

AI & Deep Learning

·       PyTorch

·       TensorFlow/Keras

·       OpenCV

·       Ultralytics YOLO

·       CNN Fundamentals

Programming

·       Python

·       NumPy

·       Pandas

·       Object-Oriented Programming

Data

·       Data Annotation

·       Dataset Preparation

·       Data Augmentation

Deployment

·       Docker

·       REST APIs

·       GPU Optimization

·       Linux

Requirements

Preferred Experience

·       Experience with live video processing.

·       Experience deploying AI models on NVIDIA GPUs.

·       Familiarity with DeepStream or TensorRT.

·       Knowledge of MLOps fundamentals.

·       Experience using Git and Agile methodologies.

Qualifications

·       Bachelor's degree in Computer Science, AI, Electronics, or a related field.

·       2–4 years of hands-on experience in AI/ML or Computer Vision.

·       Strong problem-solving and analytical skills.

Core Tech Stack

Python • PyTorch • OpenCV • Ultralytics YOLO • TensorRT • Docker • Linux • Git • REST APIs • AWS