AI/ML Engineer
Key Responsibilities: AI/ML Development & Computer Vision • Design, train, and evaluate models for: • Face detection and recognition • Object/person detection and tracking • Intrusion and anomaly detection • Human activity or pose recognition/estimation • Work with models such as YOLOv8, DeepSORT, RetinaNet, Faster-RCNN, and InsightFace. • Perform data preprocessing, augmentation, and annotation using tools like LabelImg, CVAT, or custom pipelines. Surveillance System Integration • Integrate computer vision models with live CCTV/RTSP streams for real-time analytics. • Develop components for motion detection, zone-based event alerts, person re-identification, and multi-camera coordination. • Optimize solutions for low-latency inference on edge devices (Jetson Nano, Xavier, Intel Movidius, Coral TPU). Model Optimization & Deployment • Convert and optimize trained models using ONNX, TensorRT, or OpenVINO for real-time inference. • Build and deploy APIs using FastAPI, Flask, or TorchServe. • Package applications using Docker and orchestrate deployments with Kubernetes. • Automate model deployment workflows using CI/CD pipelines (GitHub Actions, Jenkins). • Monitor model performance in production using Prometheus, Grafana, and log management tools. • Manage model versioning, rollback strategies, and experiment tracking using MLflow or DVC. • As an AI/ML Engineer, you should be well-versed of AI agent development and finetuning experience Collaboration & Documentation • Work closely with backend developers, hardware engineers, and DevOps teams. • Maintain clear documentation of ML pipelines, training results, and deployment practices. • Stay current with emerging research and innovations in AI vision and MLOps.
Required Qualifications: • Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, or a related field. • 3–6 years of experience in AI/ML, with a strong portfolio in computer vision, Machine Learning. • Hands-on experience with: • Deep learning frameworks: PyTorch, TensorFlow • Image/video processing: OpenCV, NumPy • Detection and tracking frameworks: YOLOv8, DeepSORT, RetinaNet • Solid understanding of deep learning architectures (CNNs, Transformers, Siamese Networks). • Proven experience with real-time model deployment on cloud or edge environments. • Strong Python programming skills and familiarity with Git, REST APIs, and DevOps tools. Preferred Qualifications: • Experience with multi-camera synchronization and NVR/DVR systems. • Familiarity with ONVIF protocols and camera SDKs. • Experience deploying AI models on Jetson Nano/Xavier, Intel NCS2, or Coral Edge TPU. • Background in face recognition systems (e.g., InsightFace, FaceNet, Dlib). • Understanding of security protocols and compliance in surveillance systems.
Tools & Technologies: Languages & AI - Python, PyTorch, TensorFlow, OpenCV, NumPy, Scikit-learn Model Serving - FastAPI, Flask, TorchServe, TensorFlow Serving, REST/gRPC APIs Model Optimization - ONNX, TensorRT, OpenVINO, Pruning, Quantization Deployment - Docker, Kubernetes, Gunicorn, MLflow, DVC CI/CD & DevOps - GitHub Actions, Jenkins, GitLab CICloud & Edge - AWS SageMaker, Azure ML, GCP AI Platform, Jetson, Movidius, Coral TPU Monitoring Prometheus, Grafana, ELK Stack, Sentry Annotation Tools - LabelImg, CVAT, Supervisely.
