Hi, I'm Thejesh Mallidi.
A
I am a recent Master’s graduate in Data Science from Michigan State University, with a strong focus on building NLP-driven solutions, advanced computer vision systems, and scalable machine learning models.
Skills
Detail-oriented Machine Learning Engineer with expertise in Natural Language Processing, Deep Learning, and Computer Vision. I specialize in architecting end-to-end AI solutions from data pipeline integration to model deployment that deliver measurable impact in real-world applications.
- Core Expertise: Data Science, Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Large Language Models (LLMs), Generative AI & Agentic AI, Predictive Modeling, Decision Analytics, Graph ML
- Languages: Python, R, C++, C, Bash
- AI/ML Frameworks & Libraries: PyTorch, TensorFlow, TensorRT, LangGraph, LangChain, LlamaIndex, AutoGen, Phi Data, VectorStores and Retrievers, Hugging Face Transformers, OpenCV, NumPy, Pandas
- Big Data & Streaming: Hadoop Spark, Apache Kafka
- Databases: MySQL, PostgreSQL
- Cloud & MLOps Tools: GCP, AWS, Heroku, Docker, Git, Flask, FastAPI, MLFlow, Nvidia Deepstream
Experience
Quantiphi Analytics
- Engineered an LLM-driven document ingestion pipeline for a leading telecom company processing 2M+ contract documents, extracting unstructured legal text into structured Neo4j graph format while preserving full document hierarchy.
- Implemented Agentic Contract Assist chatbot (GraphRAG) over the Neo4j graph + document store, improving query relevance by ~45% and reducing retrieval latency from seconds to sub-second responses.
- Developed and deployed CI/CD workflows on Red Hat OpenShift and GCP Cloud Run, enabling zero-downtime updates and Integrated BigQuery and GCS for scalable contract metadata storage.
- Created 10+ Re-usable Agentic components those can be used across the organization in various document handling workstreams.
Michigan State University
- Domain-Specific Knowledge Graph: Built a knowledge graph with over 2 million entities and 1.4 million relationships to accelerate hypothesis generation in plant science, leveraging NLP techniques such as Named Entity Recognition and Relation Extraction.
- LLM-Driven Entity Extraction from Scientific Texts: Achieved 94% entity extraction accuracy by Finetuning state-of-the-art domain specific BERT models for entity extraction, enabling robust identification of relevant biological entities.
- Custom BERT-Based Relation Extraction: Designed and implemented a custom BERT-based multi-relation extraction model to identify and extract multiple relationships within a single sentence.
- LLM-Powered Knowledge Graph Querying: Enabled accurate question answering over a knowledge graph using GenAI Agents and Retrieval-Augmented Generation (RAG) techniques, optimized through prompt engineering.
- Domain-Adaptive BERT Pretraining: Pretrained a BERT model on 400K+ plant science PubMed abstracts with selective masking strategy and weighted-loss to prioritize ~30K key plant terms. Achieved significantly lower MLM validation loss compared to baseline BERT.
Quantiphi Analytics
- Responsible for designing and implementing Automatic Information Extraction systems by combining custom Computer Vision and NLP LLMs in the BFSI domain.
- Delivered solutions for 10+ Proof of Concepts (PoCs)b> on document classification & extraction, object detection, image classification, and semantic segmentation tasks.
- Built end-to-end Airflow pipelines to orchestrate object detection, classification, and segmentation tasks, supporting scalable and reproducible ML model training.
- Developed insurance-focused predictive models for risk assessment and claim outcome prediction Logistic Regression, Decision Trees, Bagging, Random Forest, and Gradient Boosting Machines (GBM).
- Developed and implemented algorithms using Machine Learning and Statistical Modeling techniques to improve performance, quality, data management, and accuracy.
- Built and optimized real-time inference pipelines for Nvidia edge devices using DeepStreamb> with custom C++ plugins, achieving 27 FPSb> for latency-sensitive applications.
- Deployed and monitored 20+ Machine Learning models in production using AWS SageMaker, GCP Cloud Run & Kubernetes, Flask,and FastAPI.
Projects
This project provides end‑to‑end code for training a GPT‑style language model from scratch (or fine‑tuning an existing checkpoint) on large text corpora. It includes utilities for downloading and preprocessing data, a configurable training pipeline built on PyTorch .
- Re-implemented the full decoder-only Transformer in ~300 lines of model.py and a matching ~300 line training loop—embracing the “small, clean, interpretable” style while adding modern upgrades such as FlashAttention, gradient-checkpointing, and AMP/bfloat16 support for memory-efficient scaling.
- Scaled training for multi-GPU: Orchestrated PyTorch DDP for distributed training across nodes, streaming the 38 GB OpenWebText corpus through a custom shard-loader to pre-train a 124 M-parameter GPT model.
- Implemented configurable Top-k/Top-p sampling in the generation CLI: Giving users fine-grained control over randomness and diversity when producing sample continuations.
A GenAI‐powered, multi‐agent nutrition assistant designed to help users log meals, extract medical conditions from lab‐report images, and receive real‐time, personalized dietary guidance.
- Engineered a GenAI‐powered nutrition assistant that analyzes user chronic conditions and meal logs to deliver real‐time, personalized dietary recommendations in under 2 seconds.
- Formulated condition-to-nutrition pipeline that extracts user medical conditions from lab report images through OCR and a LLaMA-based model, then computes 10+ macro- and micro-nutrient thresholds tailored to those conditions.
- Designed a Web App that allowed users to chat with a Multi-Agent System for condition-specific food recommendations, dietary planning, and food image classification with ~90 % ingredient-recognition accuracy.
Developed a comprehensive methodology to identify deflected or damaged regions in road surfaces, enhancing road maintenance strategies with volumetric quantification of surface deformities.
- Utilized advanced data analysis, image analysis, and visualization techniques to assess and quantify road damage.
- Applied cutting-edge technologies, including Python, Vedo, and deep learning models like PointNet++ for accurate surface modeling.
- Enhanced road maintenance strategies by enabling volumetric quantification of surface deformities.
This project aims to describe the activity in a given image. The dataset used is the Flickr 8k dataset along with pre-trained models like VGG16 and ResNet for image representation.
- Utilized Natural Language Processing (NLP) techniques combined with deep learning models (CNN & LSTM).
- Implemented attention mechanisms to improve the quality of captions generated for images.
- Evaluated the model using metrics like BLEU and achieved competitive results.
- Tech Stack: NLP, Deep learning (CNN & LSTM), keras, Python
Self-driving cars require a deep understanding of their surroundings. This project focuses on semantic segmentation using neural networks to recognize roads, pedestrians, cars, and sidewalks with pixel-level accuracy.
SaveSphere is a real-time video analysis system that identifies potential threats and sends email alerts for prompt action.
- Identifying Potential Threats: SaveSphere actively recognizes possible dangers or risky situations through real-time video analysis using object detection techniques.
- Email Alert Notifications: Once a threat is detected, SaveSphere swiftly sends email alerts to facilitate prompt action.
To ensure the success of the project, two fundamental components needed implementation:
- The initial phase involved training the object detection model to identify threats in videos using Python and PyTorch.
- The subsequent phase focused on integrating the model into a web platform and enabling real-time email notifications for user safety.
For seamless integration into a web platform, Flask, React, and CSS were utilized to collectively bring the project to fruition.
Education
East Lansing, Michigan
Degree: Masters in Data Science
Duration: 08/2023 - 04/2025
CGPA: 3.95/4.0
Relevant Coursework:
- Advanced Machine Learning
- Statistical Modeling & Data Analysis
- Computer Vision
- Natural Language Processing
- Computional Optimization
Madanapalle Institute of Technology and Science
Madanapalle, India
Degree: BTech - Computer Science & Engineering
Duration: 07/2017 – 05/2021
CGPA: 9.23/10
Relevant Coursework:
- Data Structures and Algorithms
- Database Management Systems
- Operating Systems
- Python, C & Java