I'm an AI Engineer who fell deep into the agentic AI rabbit hole — and decided to stay there.
I build production AI systems: multi-agent orchestration with LangGraph and CrewAI, RAG pipelines with full LangSmith observability, autonomous research agents, and production-grade safety layers with hallucination detection and guardrails.
Currently transitioning from full-stack development (.NET + Angular) into AI Engineering — specifically the design, orchestration, and reliability of intelligent agentic systems. The principle that drives most of my recent work:
Important
Context is the failure mode of multi-agent systems. Not capability. If your agents don't share what they know, no model upgrade saves you.
I don't just call OpenAI APIs. I architect intelligent systems that scale.
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🌟 CodeMigrator AI — Multi-Agent Legacy Code Migration powered by NVIDIA Nemotron 3 Migrate enterprise legacy code (COBOL, Java EE) to Python in seconds using 5 specialist AI agents. Showcase project for the NVIDIA Nemotron Workshop at IIIT Hyderabad (May 16, 2026). Python LangGraph NVIDIA Nemotron FastAPI Next.js
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🤖 Software Engineer Team — A 24-agent Claude Code system that takes any idea to a working web app 7 core + 11 catalog + 6 critics. Wave-based orchestration. Concrete quality checklists. Works for people with zero coding knowledge. Claude Code Multi-agent No-code Open Source
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🛡️ AI Tech News Agent
Autonomous agent that posts AI news on X. It immediately started hallucinating. Then I fixed it with a multi-stage validation layer. LangGraph Gemini Tavily GitHub Actions Tweepy
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🔍 Multimodal RAG Chatbot
Production-grade RAG system on Azure with full LangSmith observability. 95% retrieval accuracy with end-to-end pipeline visibility. LangChain Azure OpenAI LangSmith Vector DB
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🤖 AI / Generative AI
☁️ Cloud & MLOps
💻 Languages & Frameworks
🗄️ Data & Storage
| Certification | Issuer | Status |
|---|---|---|
| 🎯 Azure AI Engineer Associate (AI-102) | Microsoft | ✅ Certified |
| 🎯 Azure Fundamentals (AZ-900) | Microsoft | ✅ Certified |
| 🎯 Context Engineering Foundation | Cognizant | ✅ Certified |
| 🚧 Agentic AI Solutions Architect (AB-100) | Microsoft | 🔄 In Progress |
| 🚧 Azure Developer Associate (AZ-204) | Microsoft | 🔄 In Progress |
class Ritish:
def __init__(self):
self.role = "AI Engineer"
self.company = "Cognizant"
self.location = "Hyderabad, India"
def current_focus(self) -> list[str]:
return [
"Multi-agent systems with LangGraph & CrewAI",
"MCP (Model Context Protocol) servers",
"Production-grade RAG pipelines with observability",
"Hallucination detection + guardrails",
"Agentic AI architecture patterns",
]
def shipping_soon(self) -> list[str]:
return [
"CodeMigrator AI → NVIDIA workshop @ IIIT Hyderabad (May 16, 2026)",
"Software Engineer Team → open sourcing this week",
"Agentic AI Solutions Architect (AB-100) certification",
]
def open_to(self) -> list[str]:
return [
"Senior AI Engineer roles (remote/hybrid)",
"Open source agentic AI collaborations",
"Multi-agent system architecture discussions",
"Mentoring junior AI engineers",
]
def fun_fact(self) -> str:
return "I broke my AI agent. It taught me more than any tutorial."
def say_hi(self) -> str:
return "Thanks for visiting. Let's build something that actually works."
me = Ritish()
print(me.say_hi())
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AI Agent Systems
Multi-agent orchestration · Tool use · Autonomous coding · Reliability engineering |
Production AI
RAG with observability · Hallucination detection · MLOps · Real users, real edge cases |
Open Source
Anything in the Claude Code · LangGraph · MCP ecosystem · Code migration tooling |
⭐ If you find anything useful here, drop a star — it tells me what to build more of.
Built with care · Auto-updated daily · 2026 · @Ritish017