AI Developer

We’re looking for an experienced AI Developer to join our team and drive the design, development, and deployment of advanced computer-vision and LLM-based solutions. You’ll build real-time object detection & tracking pipelines (LLM, YOLOv8/11), develop face-recognition models on video feeds with LLM support, and manage GPU-powered server infrastructure end-to-end.Key ResponsibilitiesModel Development & OptimizationDesign, train, and fine-tune LLMs, YOLOv8/11 models for multi-class object detection.Implement face-recognition pipelines leveraging state-of-the-art LLMs and embedding techniques.Optimize inference speed and accuracy for real-time video processing on GPUs.Video Analytics & TrackingDevelop multi-camera object-tracking algorithms across disparate video sources.Integrate OpenCV, DeepSORT (or similar), and custom tracking logic for robust performance.Infrastructure & DeploymentProvision and maintain Linux servers with SSH, Docker, and GPU drivers (NVIDIA CUDA/cuDNN).Automate CI/CD pipelines for model training, evaluation, and deployment.Monitor GPU utilization, troubleshoot performance bottlenecks, and ensure high availability.Collaboration & DocumentationWork closely with product managers, data engineers, and front-end teams to integrate AI services via REST/gRPC APIs.Write clear technical documentation, API specs, and best-practice guides.Required QualificationsBachelor’s or Master’s in Computer Science, Electrical Engineering, or related field.2-3+ years hands-on experience with:YOLOv8 or YOLOv11 (training, inference, transfer learning)Face recognition frameworks (InsightFace, FaceNet, ArcFace) and LLM embeddings (e.g., Hugging Face Transformers)Python ecosystem: PyTorch/TensorFlow, OpenCV, NumPy, scikit-learnStrong Linux skills: server provisioning, SSH, shell scripting.Proficiency in managing GPU servers (NVIDIA CUDA, Docker GPU containers)Experience deploying models in production (AWS/GCP/Azure or on-prem clusters).Preferred QualificationsFamiliarity with video-streaming protocols (RTSP/WebRTC).Knowledge of microservices architecture and API gateways.Exposure to MLOps platforms (MLflow, Kubeflow).Strong debugging, profiling, and performance-tuning abilities.