---
title: "Shudhanshu Ranjan - Software Engineer in India | Tal"
description: "Meet Shudhanshu Ranjan, MLE | R.A. and M.S. in CS at Stevens Tech | AI & ML Developer | Deep Learning Research Intern | GenAI | LLM | GCP | TensorFlow Certified based in India. AI stack includes claude code, github copilot, and langchain."
image: "https://tal.club/meet/shudhanshu-ranjan/share-image"
---

# Shudhanshu Ranjan on Tal

## Headline

MLE \| R\.A\. and M\.S\. in CS at Stevens Tech \| AI & ML Developer \| Deep Learning Research Intern \| GenAI \| LLM \| GCP \| TensorFlow Certified

## About

As a Machine Learning Engineer with a solid academic foundation in computer science from Stevens Institute of Technology, I specialize in building and optimizing AI\-driven solutions\. My work spans multiple domains, from reinforcement learning to NLP and computer vision, where I design, implement, and fine\-tune advanced machine learning models to solve real\-world problems\. During my tenure as a Graduate Research Assistant and various internships, I’ve developed expertise in training deep learning models, deploying generative AI \(GANs, VAEs\), and building reinforcement learning systems\. Notably, I designed an RL\-based stock trading system that achieved 2\.98x market returns and developed a misinformation classifier using large language models, driving significant advancements in performance and applicability\. My hands\-on experience with a wide range of machine learning frameworks \(TensorFlow, PyTorch\) and cloud services \(GCP, BigQuery ML, Vertex AI\) has equipped me with the practical skills necessary to deliver high\-impact, scalable AI solutions\. In addition, I possess strong capabilities in integrating AI models with cloud infrastructure, optimizing pipelines, and conducting rigorous performance evaluations to ensure efficiency in production environments\. I am passionate about contributing to cutting\-edge projects in AI, and I thrive in dynamic, forward\-thinking environments where my proficiency in Python, TensorFlow, and GCP, along with my experience in developing and scaling machine learning applications, can add value\. Feel free to connect or reach out for collaboration opportunities in machine learning and AI\. Skills: Python \(Pandas, NumPy, Scikit\-learn, TensorFlow, PyTorch, ARIMA\), Generative AI \(GANs, VAEs, GPT, BERT, T5\), Reinforcement Learning \(PPO, SAC, A2C\), SQL, GCP \(Vertex AI, BigQuery, LookML\), Docker, Kubernetes, MLflow, Weights & Biases\. GCP Profile: https://www\.cloudskillsboost\.google/public\_profiles/91be6b0b\-4498\-48c5\-9e3d\-9156a5815aa0

## Location

India

## AI stack

claude code

github copilot

langchain

langgraph

langsmith

mcp

## Work experience

Software Engineer at Chimera Technologies Private Limited, Sep 2025 \- Present · 1 y 1 mo, Bangalore, Building AI agents \(support and triage\) and servicenow MCP for one of the top pharmaceutical and biotechnology company\.

Software Engineer at Neurability Foundation, Dec 2024 \- Jul 2025 · 7 mo, United States, Investigate, architect, and maintain the full\-stack backend for an AI\-driven productivity platform, leveraging FastAPI, PostgreSQL, and a vector database to power intelligent task management Integrate and optimize large language model inference pipelines using managed ML services, enhancing responsiveness and reliability Implement observability and alerting for key system metrics, latency, memory usage, and error rates, to ensure robust performance Collaborate on features that translate complex user goals into structured, multi‑level task trees and context‑aware reminders

Machine Learning Engineer at Health Innovators, Dec 2024 \- Jul 2025 · 7 mo, United States, Develop and deploy a multimodal medical assistant capable of interpreting diverse imaging modalities and generating clinical summaries Fine‑tune and quantize conversational AI models for patient‑provider interactions, with integrated memory storage and logging for continuity Architect hybrid NLP pipelines combining rule‑based and transformer‑based approaches to extract and normalize clinical entities against medical ontologies Explore scalable retrieval‑augmented generation solutions to maintain long‑term context across patient records

Research Assistant at Stevens Institute of Technology, Jan 2024 \- Dec 2024 · 11 mo, Hoboken, New Jersey, United States, Working under the esteemed guidance of Professor Jia Xu on Natural Language Processing \(NLP\) projects\. Responsibilities include: Conducting cutting\-edge research in NLP, focusing on AI contributions for content moderation\. Collaborating with postdoctoral researcher and PhD students to design and implement experiments\. Analyzing large datasets to derive meaningful insights and improve NLP models\. Contributing to academic publications and findings at conferences\.

Deep Learning Researcher at PrayogPeti, Sep 2021 \- Jun 2022 · 10 mos, Bengaluru, Karnataka, India, Working on an NLP topic of toxic comment classification to curb internet negativity\. Published in IEEE: DOI: 10\.1109/ASSIC55218\.2022\.10088315\. Constructed a comparative study between 3 inductive Graph Neural Networks, namely TexTING, In\-GCN, and In\-GAT, to determine the best\-performing model for text classification\. Analyzed entropy in Inductive GAT models, finding higher entropy in smaller dataset like IMDB \(\~50k datapoints\) and lower initial entropy in larger dataset like DBPedia \(\~630k datapoints\), highlighting dataset size impact on model efficiency\. Identified and modified the Inductive GAT model and increased the accuracy of the model to 98\.10% on 14 different classes\.

Project Intern at iNeuron\.ai, May 2021 \- Aug 2021 · 4 mos, Bengaluru, Karnataka, India, Designed and developed UAV simulations in ROS and Gazebo, performed object detection in real\-time on those simulations\. Converted YoloV5 model to new MobileNetV3 model which gave 5\.5x faster performance on CPU, achieved better FPS\.

Project Intern at iNeuron\.ai, Nov 2020 \- May 2021 · 7 mos, Bengaluru, Karnataka, India, Developed innovative methods for satellite image masking and mismatch detection and presented a U\-Net with Inception CNN model on the satellite dataset\. Devised a novel image subtraction technique to outline the difference between images\. Detected 10 different object classes in the image and acquired MultiPolygons for detailed masked images in both 3\-band and 16\-band formats\. And evaluated performance by Jaccard similarity on the region of interest\.

## Education

Graduate Certificate, Machine Learning, Stevens Institute of Technology

Master of Science \- MS, Computer Science, Stevens Institute of Technology

[View the original profile](https://tal.club/meet/shudhanshu-ranjan)

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