Agentic AI/ML Engineer, Multimodal

What you'll do

• Train, fine-tune, evaluate, and optimize multimodal models and scalable vision/video evaluation pipelines.
• Build agentic retrieval and memory systems using vector databases and knowledge graphs.
• Curate datasets and contribute to model interpretability, observability, drift detection, and production deployment.

What they ask for

• Master’s/Ph.D. in Computer Science, AI/ML, Robotics, or equivalent industry experience.
• 2+ years of industry experience or relevant publications in CV/ML/AI.
• Strong expertise in computer vision, video understanding, temporal modeling, and VLMs; production-level Python and PyTorch skills.
• Experience with large-scale video/image data pipelines, cloud ML, MLOps, and fine-tuning open-source multimodal models.

Nice to have

• Experience with agentic/RAG pipelines, knowledge graphs, and agent evaluation or logging frameworks.
• Experience with quantized or distributed inference, multimodal RAG, temporal-spatial modeling, or edge deployment.

Tools

Python, PyTorch, AWS, HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA

AI focus

Research, fine-tuning, evaluation, and production deployment of multimodal vision and video models, VLMs, agentic AI, multimodal retrieval-augmented generation, and long-memory video analysis and search.