Job Title: AI Engineer – Video & Multimodal AI Location: USA-Remote Experience Level: 10+ Years About the Role: We are hiring a AI Engineer to spearhead the design, fine-tuning, and scalable deployment of cutting-edge AI systems, with a focus on deep learning, video intelligence, and multi-modal (vision + language) models. The ideal candidate has a strong academic foundation, preferably from Ivy League institutions—and proven experience in driving innovative AI solutions from research to production. Key Responsibilities: • Architect and lead the development of large-scale video AI and vision-language models (VLMs). • Fine-tune and optimize Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) for task-specific applications. • Scale model training and evaluation across distributed systems with an emphasis on GPU/accelerated environments. • Build and maintain robust AI pipelines for training, evaluation, benchmarking, and deployment using state-of-the-art MLOps tools. • Drive performance optimization of models for real-time inference using tools like TensorRT, ONNX, and NVIDIA Triton. • Collaborate cross-functionally with data scientists, researchers, and platform engineers to align model development with business goals. • Publish internal/external papers and contribute to IP creation and thought leadership in AI innovation. Minimum Qualifications: • MS or Postgraduate degree in Computer Science or related field (PhD preferred); strong preference for Ivy League graduates. • 10+ years of industry or research experience in AI/ML, with a focus on Deep Learning, Video AI, and multi-modal systems. • Advanced proficiency in Python and DL frameworks such as PyTorch and TensorFlow. • Deep expertise in fine-tuning LLMs and MLLMs, including prompt engineering, transfer learning, and embedding-based techniques. • Proven experience scaling AI model training and inference across multi-GPU and distributed compute platforms. • Strong hands-on knowledge of MLOps practices, including Docker, Kubernetes, MLFlow, and model serving. Preferred Skills: • Familiarity with NVIDIA’s AI ecosystem (TensorRT, Triton Inference Server, DeepStream SDK). • Experience with retrieval-augmented generation (RAG), attention-based models, and real-time video inference. • Prior experience in leading AI teams or projects and mentoring junior researchers/engineers. • Publications, patents, or open-source contributions in the field of AI/ML.