Hi, I am
Mukktinaadh Raghavarapu
Software Engineer —
AI
Infrastructure & Distributed Systems
About Me
Final-year CS undergrad (Parul University, 2026) specializing in AI Infrastructure and Distributed Systems. Published researcher — NeuroChainOps (ResearchGate, 2025), a privacy-preserving blockchain-backed MLOps framework. I've independently architected 25+ production-grade systems spanning federated learning, enterprise fintech, multimodal AI, and distributed infrastructure. Currently building AI/ML features as a Volunteer Engineer at Google Developer Group (GDG), where my work has been adopted across 5+ university projects.
25+
Projects
1
Published Paper
1100+
Challenges Solved
50+
Repos
Research Work
NeuroChainOps: A Privacy-Preserving, Blockchain-Backed MLOps Framework
Published: ResearchGate, 2025
Combines federated learning with zk-SNARKs and blockchain-backed model governance to enable privacy-first ML deployment at scale.
Tech Stack
Languages
AI / ML
Backend & Infra
Cloud & DevOps
Databases & MLOps
Core CS
Featured Projects
Veriblock-FL
Privacy-preserving distributed ML system combining federated learning with zk-SNARKs and blockchain-backed model governance.
- Reduced inter-node communication overhead by 10% using gradient compression
- Privacy-preserving aggregation via zk-SNARK proofs — basis of NeuroChainOps paper
FinAI_Advisor_Enterprise
Enterprise-grade financial advisory system with real-time market analysis and AI-powered portfolio optimization.
- Benchmarked at 1000+ concurrent users via Locust load testing with p95 latency <200ms
- WebSocket-based real-time market feed with Redis pub/sub
NASLib
Production-grade Neural Architecture Search with distributed training coordination.
- Reduced model FLOPs by ~12% through RL-guided architecture search
- Integrated MLflow for experiment tracking across distributed training runs
Deep-LOB-Agentv2
Deep learning system for high-frequency trading prediction on Limit Order Book data.
- SOTA performance on FI-2010 benchmark
- LSTM/Attention hybrid architectures
Multimodal-Emotion-Rec
Real-time multimodal AI combining facial expression and speech emotion recognition.
- 85%+ accuracy on RAVDESS and SAVEE
- Real-time inference pipeline (<100ms latency)
InsightFlowAI
AI-powered data analytics platform with automated ETL and business intelligence.
- Processes 10GB+ daily data via Apache Airflow DAGs
- 60% reduction in DB query load through Redis query-result caching (measured via Prometheus)
codepal-AI
High-performance AI-powered developer assistant with intelligent request batching.
- Reduced API latency by 40%
- Rate limiting handling 1000+ req/min
FaceSnapX-Attendance
Real-time face recognition system with cryptographic identity verification.
- 95%+ accuracy with <60ms inference
- 50+ concurrent video streams handled
Other Projects
Experience
Volunteer Software Engineer (AI)
Google Developer Group (GDG)
Oct 2024 — Sep 2025
- Designed and shipped Java/Python ML pipeline features adopted across 5+ university projects by 200+ students
- Improved model preprocessing accuracy by 10–15% through data augmentation and normalization refactoring
- Mentored 15+ open-source contributors via structured code reviews and technical documentation
- Built 3 end-to-end AI/ML prototypes covering NLP, computer vision, and data pipelines
AI/ML Engineer (Intern)
myonsitehealthcare
Internship
- Developed CI/CD automation tool to streamline deployment pipelines.
- Developed standalone AI agentic scheduling system featuring auto-assignment, route planning, and continuous learning using RAG override ingestion.
- Assisted with developing and maintaining other core internal systems.
Independent Project Developer
Self-directed
2022 — Present
- Architected 25+ production-grade systems as solo developer across AI infrastructure, backend systems, and DevOps
- Published peer-reviewed research on federated learning and blockchain-backed MLOps (NeuroChainOps, 2025)
- Deployed 6+ containerized microservice systems with CI/CD on AWS
Education
B.Tech — Computer Science Engineering
Parul University
Expected Graduation: 2026
Specialization: Big Data Analytics
Coursework: Machine Learning, Deep Learning, System Design, Distributed Systems, Cloud Computing, DSA, DBMS, Computer Networks.
Achievements
- 25+ production-grade repositories spanning AI infrastructure, backend systems, DevOps.
- Solved 1100+ total coding challenges across platforms.
- Published Research: "NeuroChainOps: A Privacy-Preserving, Blockchain-Backed MLOps Framework" (ResearchGate, 2025).
- 6+ containerized systems with CI/CD deployed on AWS.
GitHub Activity
50+
Public Repositories
Top Languages
"50+ public repositories spanning AI, backend, and DevOps"
Let's Connect
Open to full-time roles, research collaborations, and high-impact freelance projects.
Preferred locations: Bangalore, Hyderabad, Pune, Remote.
mukktinaadhraghavarapu@gmail.com
Click to copylinkedin.com/in/mukktinaadh
ConnectGitHub
github.com/mukktinaadh
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