Rahul · Lead Python / GenAI Engineer · 10+ yrs
PrincipalAbout Rahul
Rahul is a seasoned Gen AI Engineer with a decade of experience in the IT industry, specializing in Machine Learning and AI for over 8 years. His expertise lies in designing and deploying Generative AI solutions and advanced RAG strategies for chatbots. He is proficient in Python and various deep learning frameworks, with a solid foundation in cloud platforms like Azure and AWS. Rahul has successfully developed and deployed AI/ML solutions across diverse domains, including healthcare and insurance, showcasing his adaptability and technical prowess.
Core expertise
Additional skills(29)
HiveWhy hire Rahul?
Proven track record in deploying AI/LLM solutions to production.
Expertise in Advanced RAG strategies and LLM integration for chatbots.
Strong ownership of end-to-end AI project lifecycle.
Developed a POC for a chatbot using Cohere Contextual Compressor reranking and GPT 3.5 turbo 16k LLM, deployed on Streamlit via EC2.
Created an interactive FAQbot for insurance agents using sentence-transformer embeddings, FAISS indexing, and OpenAI as LLM.
Developed a statistical model for transplant wait time prediction, achieving a c-index of 0.68 using survival analysis methods.
Project highlights(6)
Chatbot on Open-Source McKinsey Documents – Gen AI Engineer
Overview: Developed a chatbot designed to answer questions based on Open-Source McKinsey documents. Responsibilities: Developed a Proof-of-Concept (POC) utilizing Cohere Contextual Compressor reranking and Cohere embeddings. Integrated GPT 3.5 turbo 16k LLM to power the chatbot, incorporating guardrails and an evaluator. Delivered a working demo on Streamlit, deployed on EC2, demonstrating practical application of the solution.
Key outcomes:
Developed a POC using Cohere Contextual Compressor reranking for improved contextual relevance.
Integrated GPT 3.5 turbo 16k LLM with guardrails and evaluator for robust chatbot performance.
Deployed a working demo on Streamlit, showcasing practical application and accessibility.
FAQBot for Insurance Agents – Gen AI Engineer
Overview: Created a FAQbot to provide interactive answers to insurance agent questions. Responsibilities: Utilized CSV data as a source and generated sentence-transformer embeddings for efficient retrieval. Implemented FAISS indexing for fast and accurate search capabilities. Developed the FAQbot with OpenAI as the Large Language Model (LLM), integrated for dynamic responses.
Key outcomes:
Successfully created a FAQbot using OpenAI as the LLM for interactive question answering.
Implemented sentence-transformer embeddings and FAISS indexing for efficient data retrieval.
Transplant Wait Time Prediction – ML Engineer
Overview: Developed a predictive model for estimating waiting times for prospective transplant recipients. Responsibilities: Created a statistical model to predict waiting time using survival analysis methods. Performed rigorous statistical tests on the dataset and conducted extensive feature engineering. Applied the Cox proportional hazards model to achieve a respectable c-index of 0.68.
Key outcomes:
Created a statistical model that accurately predicts transplant waiting times.
Achieved a respectable c-index of 0.68 using the Cox proportional method.
Unveiling Multifaceted Insights through NLP: Analysing Newspaper Articles – AI Engineer
Unveiling Multifaceted Insights through NLP — comprehensive NLP analysis on newspaper articles + data pipelines + warehouses + marts.
Key outcomes:
Achieved comprehensive understanding of newspaper articles through integration of various NLP techniques.
Successfully implemented BERT-based models for text summarization, sentiment analysis, and topic modeling.
Designed and developed data pipelines and warehouses to support NLP analysis.
Transplant Wait Time Prediction – ML Engineer
Transplant Wait Time Prediction — predictive model for prospective transplant recipients + survival analysis + statistical model.
Key outcomes:
Created a statistical model that accurately predicts transplant waiting times.
Achieved a respectable c-index of 0.68 using the Cox proportional method.
Performed rigorous statistical tests and feature engineering to enhance model performance.
10+ years of industry experience
- •Chatbot on Open-Source McKinsey Documents— Gen AI EngineerPython · LangChain · OpenAI · AWS +1
- •FAQBot for Insurance Agents— Gen AI EngineerPython · OpenAI · AWS · Azure
- •Transplant Wait Time Prediction— ML EngineerPython · scikit-learn · Azure
- •Unveiling Multifaceted Insights through NLP: Analysing Newspaper Articles— AI EngineerAzure · SQL · Scikit-learn · NLTK +10
- •Transplant Wait Time Prediction— ML EngineerAzure · ETL · Data Warehousing · Big Data +9
- •Pneumonia Detection on RSNA CXR— Deep Learning EngineerAzure · ETL · PyTorch · Python +7
- •Unveiling Multifaceted Insights through NLP: Analysing Newspaper Articles— AI EngineerAzure · SQL · Scikit-learn · NLTK +10
At a Glance
Single contract. Billed in USD.
Typically responds within 4 business hours.
Top Skills
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Rahul
GEN AI