Key Responsibilities

• Model Selection & Optimization: Identify, experiment with, and fine-tune state-of-the-art machine learning models for tasks such as video analytics, object recognition/detection, fraud detection, and multimodal generative AI. 
• Deep Learning & LLMs: Work with transformer architectures, foundation models, and generative AI to develop and enhance multimodal AI solutions. 
• Computer Vision: Develop and optimize models for real-time object detection, tracking, and recognition in video streams, ensuring performance in diverse conditions. 
• Fraud Detection and other anomaly detection: Design and implement machine learning models for anomaly detection and fraud prevention using advanced statistical and AI techniques. 
• Data Engineering & Processing: Preprocess large datasets, design efficient pipelines for real-time and batch processing, and integrate multimodal data sources (images, text, audio, video). 
• Deployment & Scalability: Deploy models in production using cloud-based or edge computing solutions, ensuring performance, scalability, and cost-efficiency. 
• Research & Innovation: Stay updated on the latest AI research, evaluate emerging models, and propose enhancements to improve performance and robustness. 
• Collaboration & Integration: Work closely with software engineers, data scientists, and domain experts to integrate AI models into end-user applications. 

Requirements

Technical Skills: 

• Strong experience in deep learning frameworks such as TensorFlow, PyTorch, or JAX. 
• Proficiency in computer vision techniques, including object detection (YOLO, Faster R-CNN), video analytics, and multimodal learning. 
• Hands-on experience with LLMs, transformers (GPT, BERT, T5, CLIP, etc.), and multimodal AI for text, image, and video synthesis. 
• Experience in fraud detection and/or anomaly detection using machine learning, pattern recognition, and risk modeling. 
• Experience in AWS SageMaker for model training and deployment preferred. 
• Familiarity with MLOps and deployment on cloud platforms (AWS and/or GCP preferred) or edge devices. 

Soft Skills: 

• Strong problem-solving and analytical thinking abilities. 
• Ability to work in a fast-paced, research-driven environment and adapt to evolving challenges. 
• Excellent communication skills for presenting findings and collaborating across teams. 

Preferred Qualifications: 

• Advanced Degree in Computer Science, AI, Machine Learning, or a related field. 
• 5 to 7 years of relevant experience.
• Experience working with autonomous systems, robotics, or edge AI is a plus.