class AIEngineer:
def __init__(self):
self.name = "Nishanth Ayyalasomayajula"
self.role = "AI Engineer & Full-Stack Developer"
self.location = "New York City 🗽"
self.current_company = "Florida Department of Agriculture and Consumer Services"
def current_projects(self):
return {
"🌱 Neurobud": "Mental health chatbot with fine-tuned GPT-4o, RAG, A/B testing",
"🧠 MoR Swarm": "Recursive multi-agent reasoning (Google MoR-inspired)",
"💡 RECAP": "Explainable AI Q&A with hybrid recursive-parallel processing"
}
def expertise(self):
return [
"Fine-tuning LLMs (GPT-4o, Llama, Mistral)",
"RAG pipelines with vector databases",
"Multi-agent reasoning systems",
"MLOps & AI infrastructure",
"Production-grade AI applications"
]
def impact(self):
return {
"users_served": "10K+ monthly queries",
"satisfaction_rate": "95%",
"cost_reduction": "66% via MLOps automation",
"systems_deployed": "Production AI chatbots at scale"
}Production chatbot with fine-tuned GPT-4o, RAG, sentiment analysis, and crisis detection
- Fine-Tuned GPT-4o-mini on 200+ mental health conversations → 15% satisfaction improvement
- RAG System with Qdrant vector DB → 40% reduction in hallucinations
- A/B Testing Framework for data-driven model optimization
- Real-Time Sentiment Analysis → 95% crisis detection accuracy
- Deployed to Production with 100+ real users
Tech: Python, FastAPI, OpenAI, LangChain, Qdrant, Next.js, PostgreSQL, Railway
🔗 View Project | 📄 Case Study | 💻 Live Demo
Inspired by Google's Mixture of Recursions research
- Self-organizing AI agents that reason recursively
- Distributed problem-solving at scale
- Explainable decision trees
Tech: Python, LangChain, OpenAI, Graph algorithms
Hybrid recursive-parallel processing for transparent AI reasoning
- Transparent reasoning paths
- Source attribution for all claims
- Hybrid processing for speed + explainability
Tech: Python, Hugging Face, Vector DBs, FastAPI
| Metric | Achievement |
|---|---|
| 🎯 Monthly Queries | 10,000+ users served |
| 😊 Satisfaction Rate | 95% user satisfaction |
| 💰 Cost Reduction | 66% AI infrastructure costs ↓ |
| 🚀 Systems Built | Production chatbots with GPT-4 + HuggingFace |
| ⚡ MLOps Pipeline | Automated model deployment & monitoring |
Key Contributions:
- Architected RAG pipelines serving 10K+ monthly queries
- Built automated MLOps infrastructure (CI/CD, model versioning, A/B testing)
- Reduced AI operational costs by 66% through optimization
- Integrated GPT-4, Hugging Face models, and Azure services into production systems
🤖 Multi-Agent Systems • Recursive Reasoning • Swarm Intelligence • Emergent Behavior |
💡 Explainable AI • Decision Transparency • Source Attribution • Interpretable Models |
📚 RAG Systems • Hybrid Search • Vector Databases • Context Optimization |
🛡️ AI Safety • Bias Detection • Robustness Testing • Ethical AI Design |
Core Focus Areas:
- 🔄 Recursive Multi-Agent Reasoning — How AI agents self-organize and reason collaboratively
- 🔍 Explainable AI — Making AI decisions transparent and trustworthy
- 📚 Retrieval-Augmented Generation — Grounding LLMs with real-time knowledge
- 🛡️ AI Safety — Building responsible, ethical AI systems
I believe the best AI engineers don't just understand algorithms — they ship systems that solve real problems at scale.
My approach:
- Research-driven: Stay at the cutting edge (papers, experiments, novel architectures)
- Production-first: Build for users, not just demos (scalability, reliability, UX)
- Measurable impact: Track metrics that matter (user satisfaction, cost reduction, accuracy)
- Responsible AI: Prioritize safety, explainability, and ethical design
I'm always open to discussing:
- 🤝 Collaborating on AI/ML projects
- 💼 Full-time opportunities in AI Engineering
- 🧠 Research in multi-agent systems & explainable AI
- 🚀 Building production LLM applications
Reach out if you're working on something cool!
🔹 AI Engineer / ML Engineer roles (Full-time, Remote/Hybrid)
🔹 Research collaborations in multi-agent systems
🔹 Consulting on RAG pipelines & LLM fine-tuning
🔹 Open-source contributions to AI/ML projects
⭐️ If you find my work interesting, consider starring my repos!