class AIEngineer:
def __init__(self):
self.name = "Abhi Gupta"
self.role = "AI Engineer | Data Scientist"
self.education = "B.E. Computer Science @ BITS Pilani"
self.interests = [
"Multi-Agent Systems",
"RAG Pipelines",
"LLM Orchestration",
"NLP & Transformers",
"Vector Databases",
"Model Context Protocol"
]
def current_focus(self):
return {
"building": "Production-grade RAG & Agentic Systems",
"learning": "LangGraph, LangSmith & GenAI",
"exploring": "MCP-Style Context Control"
}
def future_goals(self):
return "Applied AI Engineer solving real-world problems"Languages & Core
AI & LLM Engineering
ML & Deep Learning
RAG & Vector Search
Backend & Frameworks
Databases
MLOps & DevOps
- Architecting autonomous multi-agent systems with LangGraph (planning, parallelism, memory, control flow)
- Building production-grade RAG pipelines with retrieval grounding and LangSmith evaluation
- Shipping end-to-end NLP APIs with FastAPI: model to service to user
- Engineering model-agnostic LLM backends via OpenRouter for cost and latency optimization
- Orchestrating LLM context and tools using MCP-style patterns
| Certification | Issuer | Date |
|---|---|---|
| Machine Learning Specialization | Stanford & DeepLearning.AI (Coursera) | Oct 2025 |
| Data Science Specialization | IBM (Coursera) | Aug 2025 |
| Google Analytics Certification | Google Skillshop | Jun 2025 |
"Analyzing today, Predicting tomorrow!"
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