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Rahul Singh Inda
Verified Expert in Engineering
Data Scientist and AI Developer
Bengaluru, Karnataka, India
Toptal member since November 24, 2021
Bio
Rahul is a senior AI engineer with 7+ years of experience in architecting and deploying production-grade NLP and LLM systems. He is skilled in building multi-agent workflows, RAG pipelines, and evaluation frameworks. Rahul has a proven track record of driving high-performance AI/ML production systems using Python, Docker, and AWS/GCP ecosystems to improve clinical documentation and patient outcomes.
Portfolio
AB InBev
Data Science, Python, Machine Learning, Regression Modeling...
Advinow Medical
Agentic RAG Systems, Artificial Intelligence (AI), AI Model Training
Giotto.ai
Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP)...
Experience
- Python 3 - 5 years
- Generative Pre-trained Transformers (GPT) - 5 years
- PyTorch - 5 years
- Natural Language Processing (NLP) - 5 years
- Large Language Models (LLMs) - 5 years
- OpenAI - 5 years
- Retrieval-augmented Generation (RAG) - 4 years
- LangChain - 2 years
Preferred Environment
Data Science, Machine Learning, Natural Language Processing (NLP), ChatGPT, FastAPI, Gemini, Agentic RAG Systems, Retrieval-augmented Generation (RAG), LangChain, LangGraph
The most amazing...
...thing I've achieved so far was ranking number 241 among global data scientists at the Kaggle competition.
Work Experience
GenAI Engineer
2025 - PRESENT
AB InBev
- Built a multi-agent workflow using Amazon Bedrock, LangChain, and LangGraph for multi-step document reasoning, retrieval, and response generation.
- Designed agent orchestration with tool-calling, memory, and routing logic to handle domain-specific queries across internal knowledge sources.
- Architected a Gemini-based image generation service for marketing campaigns, deploying automated creation workflows for brand-aligned visual assets to drive creative production efficiency.
- Deployed advanced intent classification and entity extraction modules leveraging few-shot learning and schema-driven prompt engineering to validate AI/ML production systems rules.
- Optimized the pipeline for low-latency inference and scalable deployment using Docker and cloud-native services.
Technologies: Data Science, Python, Machine Learning, Regression Modeling, Large Language Models (LLMs), A/B Testing, Natural Language Processing (NLP), OpenAI GPT-4 API, Agentic RAG Systems, Amazon Bedrock
Senior Engineer
2024 - 2025
Advinow Medical
- Fine-tuned a base LLM using LoRA on domain-specific data to improve task performance while keeping training cost and compute usage low.
- Applied low-rank adaptation to specialize the model for classification, entity extraction, and summarization tasks in a constrained domain.
- Achieved better domain adaptation with faster training cycles compared to full fine-tuning.
- Optimized the pipeline for low-latency inference and scalable deployment using Docker and cloud-native services.
Technologies: Agentic RAG Systems, Artificial Intelligence (AI), AI Model Training
NLP Engineer
2021 - 2024
Giotto.ai
- Leveraged LLMs such as Gemini to architect retrieval-augmented generation (RAG) systems, significantly advancing the capabilities of internal medical document question-answering systems.
- Integrated vector search, SQL, and API tools to improve answer quality, traceability, and production reliability.
- Created text classification models using semantic similarity to classify documents into 100+ label categories.
- Built question-answering (QA) models using BERT and deployed them on a GPU using GCP.
- Managed models in production, including logging and error handling using Google Cloud, Docker, and Grafana.
Technologies: Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Natural Language Toolkit (NLTK), Docker, Data Science, Large Language Models (LLMs), FastAPI, Gemini, OpenAI, LangChain, Google Cloud, Retrieval-augmented Generation (RAG), Artificial Intelligence (AI), ChatGPT, Prompt Engineering
Lead Data Scientist
2021 - 2022
Skuad
- Built a neural search engine to solve users' queries using deep learning and FAISS, improving the CTR by 8%.
- Deployed and optimized search to production with around 65,000 to 80,000 daily queries and improved query auto-solves by 25%. Deployed model to production using Google Cloud.
- Implemented a pipeline to group user data based on topics and a deduplication pipeline for content and queries.
Technologies: APIs, Data Science, Docker, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Python, PyTorch, Deep Learning, Machine Learning, Retrieval-augmented Generation (RAG), Large Language Models (LLMs), Artificial Intelligence (AI), Prompt Engineering, ChatGPT, Google Cloud
Data Scientist
2019 - 2021
Embibe
- Implemented metadata tagging for academic content with graph nodes for consumer consumption, saving hundreds of person-hours.
- Built an NLP model to tag 10,000+ concepts and derive learning entities using vector-based inferencing to maximize the value of GPU and reduce response time.
- Developed an algorithm for knowledge tracing to model students' knowledge using graph embeddings. The goal is to accurately predict how students will perform in future interactions based on learning activities. The algorithm improved accuracy by 12%.
- Developed the process and led two junior employees in the launch of a doubt resolutions product for students. A vector-based search algorithm returns top-matched questions to users using text and images.
- Built a pipeline for concept tagging YouTube videos. It can download and fetch video transcripts using a text-to-speech API and create a classification model.
- Worked on Google Cloud Run to build a distributed architecture to solve the scalable deployment of deep learning models. Deployed the models with a logging and monitoring dashboard for real-time and batch inference mode.
Technologies: PyTorch, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPU Computing, Computer Vision Algorithms, Scikit-learn, NumPy, Pandas, PySpark, Artificial Intelligence (AI)
Experience
Cornell Birdcall Identification
https://www.kaggle.com/rsinda/training-efficientnet-modelParticipated in a Kaggle competition to identify a wide variety of bird vocalizations in natural landscape recordings. A recording can include multiple bird species, and the task was to predict all the species in the recordings. The audio clips contained noise from the surrounding landscape, and I built a robust model for this competition.
You can read the full description at https://www.kaggle.com/c/birdsong-recognition.
You can read the full description at https://www.kaggle.com/c/birdsong-recognition.
Product Classification API
https://github.com/rsinda/product-classificationA multi-class classifier to classify product categories from a given set of features. To build this, I trained a random forest model, used TfidfVectorizer to build textual features, dealt with class imbalance, dockerized the entire application, and developed an API.
Identify Placement of Tubes in Chest X-rays
https://www.kaggle.com/rsinda/38th-place-solution-0-972-single-model-5-foldA Kaggle competition to classify the presence and correct placement of tubes on chest x-rays to save lives. The dataset contained 40,000 images, and the task was to categorize a tube that was poorly placed. I won the silver medal in this competition.
Education
2015 - 2019
Bachelor's Degree in Computer Science
U. V. Patel College of Engineering - Ahmedabad, Gujarat, India
Certifications
FEBRUARY 2020 - PRESENT
CutShort Certified Deep Learning - Advanced
cutshort
SEPTEMBER 2019 - PRESENT
Convolutional Neural Networks
Coursera
AUGUST 2019 - PRESENT
Neural Networks and Deep Learning
Coursera
Skills
Libraries/APIs
PyTorch, NumPy, Pandas, Natural Language Toolkit (NLTK), Scikit-learn, PySpark
Tools
ChatGPT
Languages
Python 3, Python
Platforms
Ubuntu, Docker
Storage
MongoDB, Google Cloud
Frameworks
LangGraph
Other
Random Forests, Data Science, Machine Learning, Computer Vision, Retrieval-augmented Generation (RAG), Artificial Intelligence (AI), Vector Databases, Natural Language Processing (NLP), GPU Computing, Computer Vision Algorithms, Deep Learning, Neural Networks, Long Short-term Memory (LSTM), Recurrent Neural Networks (RNNs), FastAPI, APIs, Big Data, Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), Gemini, OpenAI, LangChain, Prompt Engineering, Speech Recognition, Agentic RAG Systems, AI Model Training, Regression Modeling, A/B Testing, OpenAI GPT-4 API, Amazon Bedrock
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