Hiten Jain
+91-7424855035 &bull; hitenjain0509@gmail.com &bull; linkedin.com/in/hiten-jain-b44846210
Generative AI Engineer
Enthusiastic AI graduate with a strong foundation in Natural Language Processing (NLP), Generative AI, and lar
gelanguage models. Eager to leverage expertise from Deep Learning, NLP
, and Machine Learning certifications to contribute tocutting-edge developments in the field. Passionate about the transformative potential of AI and NLP
.SKILLS
WORK EXPERIENCE
Nimbusnext &bull; Princeton, New Jersey
, United States &middot; Remote &bull; 1
1/2023 - Pr esent Junior Softwar
e AssociateRenix Informatics &bull; Remote &bull; 09/2023 - 1
1/2023 AI Resear
ch Intern EDUCA
TION Bachelor of T
echnology - BT ech in Electrical, Electr
onics and Communications EngineeringSwami Keshvanand Institute of T
echnology, Jaipur &bull; 01/2019
PROJECTS
Chat W
ith multiple PDF&#039;s &bull; 07/2023 - 08/2023
This project built a conversational interface for searching through multiple PDFs. It utilized Langchain for the framework,
Huggingface for text embeddings, and Faiss-CPU for ef
ficient text storage and retrieval. To generate context-aware responsesto user queries, it implemented lar
ge language models like google/flan-t5-xxl, GPT4All, and Lamini. Finally , a memoryfeature was incorporated to enable natural and coherent interactions across user queries.
Boolean Question Answering System &bull; 03/2022 - 04/2022
AI model for answering yes-or
-no questions. The model is trained using an encoder -decoder architecture with LSTM layers.It learns from a collection of questions and their corresponding &quot;yes&quot; or &quot;no&quot; answers. The text is first prepared and turned
into numerical representations, then these are used to train the model to predict answers for future yes-no questions. Azure Openai
&bull;
Deep Learning
&bull;
Django
&bull; Langchain
&bull;
Lar
ge Language Models (LLM) &bull;
Llamaindex
&bull; Machine Learning
&bull;
Natural Language Processing (NLP)
&bull;
Python
&bull;
Implemented retrieval-based techniques like RAG, resulting in a 25% increase in information retrieval ef
ficiency andultimately enhancing productivity for the team.&bull;
Designed and implemented generative AI applications leveraging cloud platforms like Azure and web frameworks like
Django.&bull;
Customized LLMs to meet project-specific requirements, enhancing the overall quality and functionality of software
products.&bull;
Prepared POCs, finetuned GPT
, and used other various techniques like RAG.&bull;
Gained experience in all aspects of AI research, from data collection and preparation to model development and
evaluation.&bull;
Collaborated with a team of experienced researchers to solve complex AI problems.
&bull;