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.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Full Stack Developer - Averlon