class SoftwareEngineer:
def __init__(self):
self.name = "Jeet Patel"
self.role = "Software Engineer | AI/ML Engineer"
self.education = "M.S. Data Science @ Indiana University (GPA: 3.8)"
self.location = "Bloomington, IN"
def current_focus(self):
return [
"Building production ML systems that scale",
"Backend services with AI/ML integration",
"Distributed systems and inference optimization",
"MLOps and reliable model deployment"
]
def technologies(self):
return {
"languages": ["Python", "Go", "Java", "SQL"],
"backend": ["FastAPI", "Django", "REST APIs", "gRPC"],
"ml_stack": ["PyTorch", "TensorFlow", "LangChain", "HuggingFace"],
"databases": ["PostgreSQL", "Redis", "MongoDB", "Neo4j"],
"cloud": ["AWS", "GCP", "Docker", "Kubernetes"],
"mlops": ["MLflow", "Airflow", "CI/CD", "Monitoring"]
}Building production AI systems for nonprofit analytics
- Built LLM-powered chatbot handling 1,200+ monthly queries with 90%+ accuracy
- Deployed Text-to-SQL service using Llama 3 with LangChain and FAISS
- Implemented Mistral-7B pipeline with chain-of-thought reasoning for mission classification
- Designed distributed processing system for 175K+ nonprofit records across GPU clusters
- Built Neo4j knowledge graph revealing 78 latent nonprofit funding networks
Optimizing AI inference and building scalable ML systems
- Reduced inference latency by 18% through batch processing optimizations
- Built RAG-powered chatbot with vector search (FAISS/ChromaDB)
- Developed BERT-based classification model achieving 80% accuracy
- Engineered DynamoDB system handling 500K+ creator-brand records
- Implemented LoRA-based fine-tuning enabling weekly model updates
|
Production-grade API achieving 1,600x latency reduction through intelligent caching Highlights:
Stack: vLLM, FastAPI, Redis, Prometheus, Docker |
Production-grade backend demonstrating Stripe-style payment patterns Highlights:
Stack: Go, PostgreSQL, Redis, Docker |
|
LangChain ReAct agents for intelligent startup pitch evaluation Highlights:
Stack: LangChain, OpenAI GPT-4, Streamlit, Python |
End-to-end ML system for Medicare billing prediction Highlights:
Stack: PyTorch, XGBoost, MLflow, Delta Lake, SHAP |
|
Scalable ETL pipeline for financial fraud detection Highlights:
Stack: AWS, PySpark, SageMaker, Redshift, Lambda |
End-to-end data pipeline for climate insights Highlights:
Stack: Snowflake, dbt, Tableau, Python |
📝 I Built a Subscription Backend Like Stripe in 6 Hours: Here's What I Learned
Building something interesting? I'm always open to discussing software engineering, ML systems, or potential opportunities.
jeetp5118@gmail.com · LinkedIn · Medium