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GodVilan/README.md

Portfolio LinkedIn Medium Email Status


πŸ‘‹ About Me

profile = {
    "name"         : "Srikanth Reddy Nandireddy",
    "role"         : "Applied AI / LLM Engineer",
    "education"    : "M.S. Data Science & AI β€” University of Central Missouri",
    "gpa"          : 4.0,
    "location"     : "United States",
    "open_to_work" : True,
    "available"    : "Immediately",
    "focus"        : ["Multi-Agent Orchestration", "LLM/RAG Systems", "MLOps"],
    "frameworks"   : ["Microsoft Agent Framework", "MCP", "ReAct", "FastAPI"],
    "recent_win"   : "HackerRank Orchestrate β€” 16 / 1,773 (Top 1%)",
    "portfolio"    : "https://srikanthreddynandireddy.me",
    "gfg_streak"   : "255 consecutive days",
}

I build agentic AI systems that ship β€” multi-agent pipelines with Critic/Verifier loops, MCP-grounded retrieval, layout-aware RAG, and the MLOps automation that takes them from notebook to a live /predict endpoint you can hit with curl.


πŸš€ Featured Projects

🧠 TalentFabric AI

5-agent Microsoft Agent Framework system β€” Planner-Executor + Critic/Verifier loop-back that flips Not Ready β†’ Ready, MCP-grounded citations, all three Microsoft IQ layers. 1.0 readiness/risk accuracy on the ground-truth set.

Microsoft Agent Framework MCP Azure OpenAI RAG Streamlit

GitHub Demo

πŸ›°οΈ Multi-Modal Evidence Review

Two-stage VLM agent for insurance-claim adjudication, injection-safe by design (7/8 attacks blocked). Ranked 16 / 1,773 β€” Top 1% at HackerRank Orchestrate. 95% valid-image accuracy at ~$0.017/claim.

Claude Sonnet 4.6 VLM Prompt Engineering Eval-Driven Dev

GitHub

πŸ” arXiv Agent β€” ReAct Research Partner

Cognitive ML research agent: multi-hop ReAct planning, layout-aware RAG, adversarial double-blind peer review, recursive citation tracing. MRR@5 0.990 β€” 7.3Γ— answer relevance over BM25.

ReAct FAISS BGE Gemini SQLite

GitHub

πŸ›‘οΈ Real-Time Fraud Detection (MLOps)

Production MLOps system β€” automated CI/CD retraining, MLflow Model Registry, Dockerized FastAPI on AWS EC2 behind Nginx, with /predict + /predict-batch and SMOTE-handled class imbalance.

MLflow FastAPI Docker AWS GitHub Actions XGBoost

GitHub

🦠 US COVID-19 Mortality Forecasting   Live

SARIMAX vs Prophet vs XGBoost on 3,300+ records. Holdout MAE 138.14 β‰ˆ CV MAE 137.86 β€” near-zero overfitting. Plotly Dash dashboard on Render.

SARIMAX Prophet XGBoost Plotly Dash Docker

GitHub

🎭 Multimodal Emotion Recognition

BERT + Wav2Vec2 + ViT cross-modal Transformer fusion (4 layers, 8 heads). Weighted F1 60.66% on 7-class MELD, class-weighted loss for imbalance.

PyTorch BERT Wav2Vec2 ViT HuggingFace

GitHub

Also on the bench: πŸ“° Fake News Detection β€” TF-IDF vs DistilBERT on 62K+ articles with dynamic F1-threshold tuning Β· πŸ“Š arXiv RAG Benchmark β€” empirical study of 4 retrievers Γ— 3 chunk sizes over 150 papers


πŸ› οΈ Tech Stack

Languages

Python SQL R C++ Bash

Agents & LLM Systems

Microsoft Agent Framework MCP ReAct RAG FAISS

ML & Deep Learning

PyTorch TensorFlow HuggingFace Scikit-learn XGBoost

Cloud & MLOps

AWS Azure OpenAI MLflow Docker FastAPI GitHub Actions


πŸ“Š GitHub Stats

GitHub Streak


πŸ† Honors & Achievements

Award Details
πŸ₯‡ Graduate Student Achievement Award 2025–2026 University of Central Missouri Graduate Studies β€” Recognized for exceptional scholastic achievement, exemplary contributions to Data Science & AI, and outstanding character and leadership
πŸ… HackerRank Orchestrate β€” Top 1% Ranked 16 / 1,773 Β· 24-hour AI agent hackathon (June 2026)
πŸ₯‡ Global Rank 1 CodeChef May Long Two β€” Division 4 Rated
πŸ† National Finalist IICC Coding Competition β€” Top 1% of 100,000+ participants
πŸ₯‰ Bronze Medal Ranked Top 3 in B.Tech Academic Program
⭐ 5β˜… HackerRank Data Structures & Algorithms
πŸ”₯ 255-Day Streak GeeksforGeeks Problem of the Day

πŸŽ“ Education & Certifications

Degree Institution GPA
M.S. Data Science & AI (2024–2026) University of Central Missouri 4.0 / 4.0
B.Tech CSE (Hons.) (2020–2024) CMR College of Engineering & Technology 9.06 / 10.0

Certifications: Microsoft Applied Skills β€” Developing Agents in Microsoft Foundry Β· Anthropic β€” Model Context Protocol Β· Anthropic β€” AI Fluency Β· AWS Academy β€” Data Analytics & Cloud Foundations Β· Google Data Analytics


πŸŽ–οΈ Verified Badges

AI Skills Fest 2026 AWS Academy Graduate - AWS Academy Data Analytics AWS Academy Graduate - Cloud Foundations - Training Badge


πŸ“¬ Let's Connect

I'm actively seeking full-time Applied AI / LLM Engineer & ML Engineer roles in the United States β€” available immediately.

Portfolio LinkedIn Medium Email

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  1. arXiv-rag arXiv-rag Public

    Benchmarked RAG pipeline over 150 arXiv ML papers β€” compares BGE, MiniLM, MPNet, and BM25 across chunk sizes with MRR, Faithfulness, and Answer Relevance metrics. Powered by FAISS and Gemini.

    Python 1

  2. Real-Time-CCFDS-With-MLOPs Real-Time-CCFDS-With-MLOPs Public

    Production MLOps system for real-time credit card fraud detection β€” XGBoost + SMOTE, MLflow model registry, FastAPI inference, CI/CD via GitHub Actions, and AWS (EC2 + RDS + S3) deployment.

    Python 1

  3. Fake-News-Detection-Using-TF-IDF-Logistic-Regression-and-DistilBERT-Transformer-Models Fake-News-Detection-Using-TF-IDF-Logistic-Regression-and-DistilBERT-Transformer-Models Public

    Classical NLP vs. Transformers benchmark for fake news detection β€” TF-IDF + Logistic Regression vs. fine-tuned DistilBERT, dynamic threshold tuning, feature explainability, live NewsAPI integration…

    Python 1

  4. Multimodal-Emotion-Recognition-MELD-via-Cross-Modal-Transformers Multimodal-Emotion-Recognition-MELD-via-Cross-Modal-Transformers Public

    Multimodal emotion recognition on MELD using cross-modal Transformer fusion of BERT, Wav2Vec2, and ViT β€” staged fine-tuning, AMP, cached tensor preprocessing, 60.66% Weighted F1 on 7-class imbalanc…

    Python 1

  5. US-COVID-19-Mortality-Intelligence-Forecasting-System US-COVID-19-Mortality-Intelligence-Forecasting-System Public

    Epidemiological forecasting pipeline benchmarking SARIMAX, Prophet, and XGBoost on US COVID-19 mortality data β€” 5-fold rolling CV, 95% CI, interactive Dash dashboard, Docker + Render deployed.

    Python 1

  6. COVID-19_Deaths_Analysis_US COVID-19_Deaths_Analysis_US Public

    Jupyter Notebook