Meta Engineer on Industrial Computer Vision systems

Join our next Virtual conference Agents in Production November 18th: https://home.mlops.community/home/events/agentsinproduction2025-mlops-prosus Join our next Virtual conference Agents in Production November 18th: https://home.mlops.community/home/events/agentsinproduction2025-mlops-prosus Huge shout out to our sponsors @rafaysystems7900 , @humanloop8511 , @premai_io, @tryarcade @Deepgram //Abstract High-quality data collection remains one of the biggest bottlenecks in deploying AI systems at scale. Today, nearly 54% of AI projects stall at the proof-of-concept stage due to prolonged data acquisition challenges. In industries like manufacturing and industrial automation, gathering just a handful of images for object detection tasks can take six months to a year, given the complexity of these environments and the need for highly reliable models. With the rise of generative AI, synthetic data presents a transformative approach. This can accelerate data collection, reduce development cycles, and enable faster deployment of robust AI models in production. //Bio I am presently working as an Applied Research Scientist at Facebook AI research, working on building Multimodal foundational generative AI models. I am also interested in the domain of self supervised learning. I routinely work with billion scale datasets to train these massive multimodal models. Previously I worked as Quantitative Researcher at Citadel where my work involved leveraging the power of Machine Learning and Statistical methods in an attempt to fathom the enigmatic world that is the financial markets. I have also worked at Amazon Alexa AI on large scale multimodal models and Embodied AI applications to bring smart robot intelligence to Alexa devices. An MLOps Community Production sponsored by Humanloop & Rafay