RACE START SEQUENCE

// DRIVER PROFILE - INITIALIZING

Mythri Shivakumar

Pushing systems to the limit. Lap after lap.

β–Ά View Lap Times
0Track Experience
0Sprint Velocity Gained
0Pipeline Reliability
0Runtime Reduced
0Qualifying Score (GPA)
// SECTOR 01

About

I build AI systems that don't just work - they race. From architecting multi-agent RFP pipelines at Saccharo AI to modernizing COVID simulation engines at UB, I operate at the intersection of production engineering and bleeding-edge AI. Currently pushing laps at Saccharo AI, New York.

// SECTOR 02

Lap Times

Saccharo AI
Full Stack Engineer
March 2026 – Present
New York, NY
University at Buffalo
Research Associate
Feb 2025 – March 2026
Buffalo, NY
Infosys // Client: Apple
Software Development Engineer
Aug 2022 – Jul 2024
Hyderabad, India
Freelance Software Consultant
Independent Consultant
Aug 2020 – Aug 2022
Hyderabad, India
// SECTOR 03

Constructor Cards

⭐ FEATURED // RAG SYSTEM
DocuQuery
Production Project
RAG Document Intelligence System - Hybrid vector search with recursive splitting, Cohere reranking, and async SSE streaming for sub-2s query latency.
+30% Answer Precision <2s Latency Hybrid Vector Search
FastAPI Β· OpenAI Β· Pinecone Β· LangChain Β· React Β· Docker Β· AWS
⭐ FEATURED // AGENTIC PLATFORM
Share Cycle
Production Project
Agentic Support & Orchestration Platform - Built concept-to-production in 24 hours using an AI-native development workflow with full auth and data layer.
24hr Build-to-Prod AI-Native Workflow Full MVP + Auth
Django REST Β· React Native Β· PostgreSQL Β· OpenAI Β· Auth0 Β· Redis
πŸ† FEATURED // F1 PERSONAL
F1 Race Prediction Model
Personal Project
ML model predicting top-5 race finishers using lap delta, weather conditions, and historical race data. Ensemble approach with gradient boosting.
85% Accuracy Top-5 Prediction Lap Delta + Weather
Random Forest Β· Gradient Boosting Β· Python Β· Pandas Β· Scikit-learn
// AI/ML - COMPUTER VISION + LLM
Mood Sync
Dec 2025 – Jan 2026
  • Real-time facial emotion recognition pipeline - 95% precision across 6 emotional classes
  • Pinecone vector search with 0.9s average recommendation latency
  • LLM reasoning layer - >85% user-rated relevance in pilot testing
  • 30% CPU latency reduction via multithreading + frame skipping; 22–24 FPS live inference
Python Β· Pinecone Β· LLM Β· OpenCV Β· Multithreading
// AI/ML - GENERATIVE VISION
VAE for Emoji Generation
Jun – Jul 2025 Β· University at Buffalo
  • Convolutional VAE in PyTorch on 12,000+ emoji images - 89% reconstruction fidelity (SSIM)
  • Reparameterization trick enabling smooth latent interpolation across emotional dimensions
  • 27% reconstruction loss reduction via KL annealing; Ξ²-VAE improved separability by 22%
PyTorch Β· VAE Β· Ξ²-VAE Β· Convolutional Networks
// AI/ML - CONTRASTIVE LEARNING
Image-Caption Retrieval (SimCLR)
Mar – May 2025 Β· University at Buffalo
  • SimCLR retrieval engine over 31,000 image-caption pairs - 68% Top-5 accuracy
  • PCA dimensionality reduction 512β†’64 retaining 93% variance; t-SNE cluster visualization
  • Streamlit demo with 1.2s average query latency
SimCLR Β· PyTorch Β· PCA Β· t-SNE Β· Streamlit
// AI/ML - NLP SUMMARIZATION
Congressional Bill Summarization (BART)
Apr 2025 Β· University at Buffalo
  • Fine-tuned BART on 22,000+ congressional docs - ROUGE-L +11% over baseline
  • 18% hallucination reduction via preprocessing; ROUGE-2: 18.6 / ROUGE-L: 41.2
  • Dynamic truncation + batching for docs exceeding 1,024-token limit
BART Β· HuggingFace Β· NLP Β· PyTorch
// AI/ML - NLP MULTI-DOC
Multi-News Summarization (BART)
Apr 2025 Β· University at Buffalo
  • Fine-tuned facebook/bart-base on Multi-News - ROUGE-L 38.7
  • 21% inference time reduction via beam-width tuning; Gradio app with 2.6s response
  • Deployed reproducible pipeline via Hugging Face Hub
BART Β· Gradio Β· HuggingFace Hub Β· NLP
// AI/ML - TIME SERIES
Air Quality Prediction (LSTM)
Mar 2025 Β· University at Buffalo
  • LSTM forecasting hourly pollutants - RMSE βˆ’19% over linear regression
  • Lag feature engineering +14% stability; RΒ² improved 0.61 β†’ 0.79
  • LSTM vs GRU comparison: 12% faster convergence observed in GRU
LSTM Β· GRU Β· PyTorch Β· Time Series Β· Pandas
// AI/ML - DEEP LEARNING
VGG & ResNet from Scratch
Feb 2025 Β· University at Buffalo
  • VGG: 87% test accuracy Β· ResNet: 90% test accuracy - no pretrained weights
  • 29% faster convergence via batch norm + LR scheduling; 18% overfitting reduction
  • Grad-CAM validation: 88% of correct samples activated on correct regions
PyTorch Β· VGG Β· ResNet Β· Grad-CAM Β· CNN
// AI/ML - REINFORCEMENT LEARNING
RL Vacuum Cleaner Bot
Nov 2024 Β· University at Buffalo
  • 6Γ—6 grid-world with stochastic transitions - SARSA 24% faster convergence vs Q-Learning
  • Double Q-Learning reduced overestimation bias by 17%
  • Stable optimal policy learned within 450 training episodes
SARSA Β· Q-Learning Β· Double Q-Learning Β· Python
// AI/ML - COMPUTER VISION
EMNIST Character Recognition (CNN)
Oct 2024 Β· University at Buffalo
  • Custom CNN - 92.4% test accuracy on EMNIST Balanced
  • +11% validation accuracy via augmentation + dropout; training loss βˆ’33% via LR scheduling
  • ROC + confusion matrix analysis identified top 3 confusion clusters for architecture tuning
PyTorch Β· CNN Β· EMNIST Β· Data Augmentation
// DATA - REAL ESTATE ML
Real Estate Sales Prediction
Feb 2025 Β· University at Buffalo
  • Feature engineering on 1.09M+ transactions - 31% missing data reduction
  • Gradient Boosting + XGBoost: RΒ² 0.86, RMSE βˆ’17% vs baseline
  • Bayesian hyperparameter optimization +9%; Tableau dashboards across 150+ neighborhoods
XGBoost Β· Gradient Boosting Β· Bayesian Opt Β· Tableau Β· Python
// DATA - INFRASTRUCTURE
Environmental Data Warehouse
Mar – Apr 2025 Β· University at Buffalo
  • Normalized schema: 10 relational tables for structured environmental analytics
  • 34% query performance improvement via indexing + EXPLAIN ANALYZE; stored procs βˆ’42% repetition
  • Join-heavy query optimization: 2.3s β†’ 1.4s execution time
PostgreSQL Β· SQL Optimization Β· Data Warehousing Β· Indexing
// DATA - PRICING ANALYTICS
Airbnb Price Prediction
Sep – Nov 2024 Β· University at Buffalo
  • EDA on 48,000+ Airbnb listings - identified top 5 price-driving features
  • Random Forest: +16% accuracy over linear baseline; MSE βˆ’21% via feature engineering
  • Cross-validated RΒ² of 0.77 across folds
Random Forest Β· Feature Engineering Β· EDA Β· Python Β· Scikit-learn
// SECTOR 04

Telemetry

⚑ Power Unit - Core Languages
PythonTypeScript JavaScriptSQL GoJavaC++
🌬 Aerodynamics - AI/ML Stack
LangGraphLangChain RAG (Hybrid)Multi-Agent Systems DSPyOpenAI API Claude APIPineconeLanceDB
πŸ”© Chassis - Frameworks
FastAPIDjango FlaskReact / Next.js 14 Node.jsReact Native
πŸ”Œ Electronics - Infrastructure
AWS (Lambda/S3/EC2/ECS) DockerKubernetes KafkaRedis PostgreSQLMongoDB
πŸš€ DRS System - AI-Native Workflow
AI-Augmented PR Synthesis Claude-Driven Refactoring Cursor (Advanced) LLM-Assisted Testing
// SECTOR 05

Driver Academy

University at Buffalo, SUNY
MS Computer Science - AI Specialization
Aug 2024 – Dec 2025 Β· Buffalo, NY
3.87
/ 4.0 GPA
JNTU Hyderabad
B.Tech Computer Science
Jul 2018 – Aug 2022 Β· Hyderabad, India
3.5
/ 4.0 GPA
// SECTOR 06

Homologation

☁️
Microsoft Certified: Azure Developer Associate
πŸ”·
Microsoft Certified: Azure Fundamentals
🟑
AWS Certified Cloud Practitioner
πŸ“„
Publication: Cognitive NLP Assistant for Post-Operative Care
doi.org/10.26562/IRJCS.2021.V0806.001
🏁
// RACE CONTROL

Let's Connect

Email
geetamythri27@gmail.com
Phone
(716) 319-4689
LinkedIn
mythri-shivakumar
GitHub
MythriShivakumar

Β© 2026 MYTHRI SHIVAKUMAR // BUILT AT THE PIT WALL

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