from universe import engineer
class Dharaneesh(engineer):
def __init__(self):
self.name = "Dharaneesh N"
self.location = "Coimbatore, India"
self.degree = "B.Tech in AI & ML"
self.role = "AI / ML Engineer"
self.stack = ["Python", "PyTorch", "LangChain",
"FastAPI", "Docker", "Kubernetes"]
self.currently = ["GraphRAG", "Distributed Systems",
"LLM Agent Orchestration"]
self.fun_fact = "I build agents that build other agents"
self.superpower = "Shipping production AI, not just notebooks"
def connect(self) -> str:
return "dharaneesh794@gmail.com"
| Project | Stack | Highlights |
|---|---|---|
| MultiAgent Orchestrator | LangGraph + FastAPI + Kubernetes | 5-agent DAG with hallucination scoring, parallel fan-out, automatic retry. 12-container K8s stack with Prometheus/Grafana, OpenTelemetry, CI/CD with rollback |
| MLOps Pipeline | PyTorch + Optuna + Kubernetes | Feature store (PostgreSQL + Redis), automated Optuna tuning, model registry with staging-to-production gates, drift detection dashboards |
| MedGraph RAG | FAISS + TigerGraph + Groq LLaMA | FAISS RAG, GraphRAG & Hybrid QA. 2-hop retrieval with BERTScore. 5000+ vector embeddings for retrieval benchmarking |
| Achievement | Details | |
|---|---|---|
| 🏅 | TigerGraph x BuilderBase GraphRAG Hackathon | Participant (2026) |
| 🏅 | Scaler OpenEnv Hackathon | Participant (2025) |
| 🤗 | Hugging Face Spaces | Deployed AI/RL applications |
| 🌍 | Open Source | Contributed to langchain/langgraph |
| Degree | Institution | Year | Score |
|---|---|---|---|
| B.Tech – Artificial Intelligence & Machine Learning | SNS College of Technology, Coimbatore | 2024 – Present | CGPA 8.00 |
🧠 GraphRAG → TigerGraph + FAISS Hybrid Retrieval
⚙️ Distributed Systems → Kubernetes + Kafka + Celery
🤖 LLM Agents → LangGraph + Multi-Agent Orchestration
