I’m a Machine Learning Engineer on the ML Infrastructure team at SambaNova Systems, driving high-priority model bring-ups and urgent customer requests, as well as strategic ML pipeline automation and infrastructure efficiency initiatives. Collaborating on cross-layer projects, I accelerate end-to-end development velocity on Reconfigurable Dataflow Units (RDUs), deploying Generative LLMs (Language & Multimodal) training, pre-training, fine-tuning, and inference—for developers, national laboratories, and private enterprises within critical deadlines over Cloud & On-Prem. My expertise stems from a funded robotics master's at Carnegie Mellon University (CMU) Robotics Institute - School of Computer Science with Computer Vision (CV), Deep Learning (DL) and Machine Learning depth and Software Engineer experience at JBT Corp. My unwavering commitment to solving complex real-world Artificial Intelligence (AI) challenges, coupled with a strong desire to democratize affordable and accessible technology, drives career aspirations in my speciality. Given my strong academic background, prestigious accolades, 5+ years of extensive practical experience developing industry-relevant solutions in diverse CV, DL and ML global ventures, with over 9 publications, combined with my demonstrated track record of soft skills, I am an excellent candidate for a relevant position.

At CMU, I cultivated proficiency in creating practical AI solutions that align seamlessly with business principles while effectively serving client needs. My experience spanned 3 diverse labs, coordinated with Pitt and UPMC physicians, sponsored by DoD, NSF and DARPA, emphasizing 3 industry-scale AI challenges: 1. Transparency, deployability and resource limitations for AI adoption: Heuristics-guided explainable AI; 2. Precision inadequacy in automated robotic interventions: Physics-informed generative AI; 3. Vast unlabelled and unstructured training data: Contrastive unsupervised representation learning. Moreover, through course projects, I explored enabling shuffled position embeddings in ResNet-ViT and zero-shot visual place recognition with foundation models. Summer of 2023, interning at JBT, collaborating with ifm, Oppent, and OSU, I integrated an O3R camera with autonomous vehicle navigation-vision C++ stack on edge VPU, enabling obstacle detection, generating requirements, reducing stack lag and streamlining verification & validation protocols while leveraging agile practices, version control systems and CI/CD pipelines.

Before CMU, several global collaborations with universities: University of Waterloo, NUS, ICL and CUHK (exchange study) exposed me to designing industrial automation software: A clinical diagnosis and experimental analysis app to aid ophthalmologists and CV-DL frameworks to tackle automation delays in medical imaging (Graph networks) and latency in computer-assisted surgery (Paced curriculum learning with knowledge distillation). My introduction to CV, DL and ML was at IIT-M, building sketch query 3D CAD model CNN retrieval systems and honed my skillset at Origin Health Pte. Ltd., prototyping in-house Python GUI and fetal screening CV-algorithms that piqued VCs. Moreover, I garnered this passion initially, devising a DL-based EMG-controlled CPM machine, as an undergraduate instrumentation and control engineer at my university robotics club. These experiences made me industry-ready, equipped with organizational ethos and AI expertise in signal, image, video and volume data.

My extensive practical experience, robust academic background, accolades, and diverse soft skills make me a valuable asset to any suitable role. Don't hesitate to contact me for any additional questions or further clarification on my achievements. Thank you for your time and consideration.

• 💬 Ask me about: Anything tech
• 📫 How to reach me: Drop a mail
• ⚡ Fun fact: Most people pronounce my first name wrong. Give it a try 🙃
• ♟ Hobbies: Chess, Athletics and Music