
Naman Mohnot
Machine Learning Intern

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About me
Senior Product Engineer, Applied AI, Sprinklr | CSE, IIT Madras
Education

Delhi Public School, Jodhpur
2015 - 2016Matriculation CBSE 10 CGPA
Indian Institute of Technology, Madras
2018 - 2022Bachelor's degree Computer Science
Jayshree Periwal High School, Jaipur
2016 - 2018Intermediate +2 Science and Maths 94.4%
Experience

Ayu Devices Pvt Ltd
Dec 2019 - Jan 2020Machine Learning InternSuccessfully classified heart sounds as normal/abnormal using an LSTM model in TensorFlow, leveraging pre-processing techniques such as Hidden Markov models, and enhancing accuracy with noise reduction using signal processing algorithms

Sprinklr
May 2021 - Jul 2021Summer Intern
Sprinklr
Jul 2022 - now๐ฆ๐บ๐ฎ๐ฟ๐ ๐๐ผ๐บ๐ฝ๐ผ๐๐ฒ:โข Led the development of a Gen-AI enhanced "typing aid" feature to provide precise suggestions for customer support agents. โข Innovatively created a high-performing scoring mechanism using causal LMs and brand data, enhancing precision twofold. Utilized optimization strategies such as mixed precision, kernel fusion, ONNX, ONNX Runtime, and the MQTT protocol to reduce system latency by more than 50%. Scaled this system from 20 to 500+ clients within a week.๐ฆ๐บ๐ฎ๐ฟ๐ ๐ฅ๐ฒ๐ฝ๐น๐:โข Created an SOP-grounded next response generation feature, assisting customer case agents.โข Utilized a "patent-pending" method to derive agent handbooks and knowledge bases from conversation data via clustering and LLMs. Employed advanced optimization tools such as MLC and TensorRT, leveraging quantization, precision optimization, and hardware acceleration. โข These methods enabled in-house models to achieve 95% accuracy at 10x lower cost compared to GPT-4 and generated a new revenue stream through integration into Sprinklr's AI+ suite.๐๐ต๐ฎ๐-๐๐ฎ๐ถ๐น๐๐ฟ๐ฒ๐ฎ๐ธ๐ฒ๐ฟ: โข Developed a novel โJailbreaking Chat Simulationโ technique to train, test, and evaluate Gen-AI bots. Leveraged powerful LLMs to simulate conversations aimed at breaking guardrails, generating a "jail-break proof" dataset. โข This significantly enhanced model adherence to guardrails and knowledge bases. Boosted model accuracy from 65% to 95%, facilitating the full transition of voice bot volume to the new model. Automated testing reduced time from a full day to 1-2 hours, greatly improving efficiency and expediting partner onboarding. Show less
Senior Product Engineer
Jun 2024 - nowProduct Engineer
Jul 2022 - May 2024
Licenses & Certifications
- View certificate

Deep Learning Specialization
CourseraJun 2020 - View certificate

Sequence Models
CourseraJun 2020 - View certificate

Convolutional Neural Networks
CourseraMay 2020 - View certificate

Structuring Machine Learning Projects
CourseraMay 2020 - View certificate

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
CourseraMay 2020 - View certificate

Neural Networks and Deep Learning
CourseraApr 2020 - View certificate

Machine Learning
CourseraSept 2019
Languages
- hiHindi
- enEnglish
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