Job
Description
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 Responsibilities Model Development & Optimization Design, 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 & Tracking Develop multi-camera object-tracking algorithms across disparate video sources. Integrate OpenCV, DeepSORT (or similar), and custom tracking logic for robust performance. Infrastructure & Deployment Provision 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 & Documentation Work 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 Qualifications Bachelor’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-learn Strong 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 Qualifications Familiarity 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. Job Types: Full-time, Permanent Pay: ₹800,000.00 - ₹1,200,000.00 per year Benefits: Flexible schedule Paid sick time Paid time off Provident Fund Location Type: In-person Schedule: Fixed shift Ability to commute/relocate: Mumbai, Maharashtra: Reliably commute or planning to relocate before starting work (Preferred) Experience: AI: 2 years (Preferred) Work Location: In person Speak with the employer +91 9830000000
