Hi, I'm Suman
|
AI Engineer specializing in Agentic Systems, RAG pipelines, and production-grade LLM applications.
I design and deploy scalable AI systems that automate complex workflows using multi-agent architectures, vector databases, and cloud-native infrastructure.
Built systems handling 19K+ images, deployed AI pipelines with Docker & CI/CD, and developing real-time agentic AI platforms.
Autonomous Multi-Agent AI System for Enterprise Knowledge Automation
Problem
Enterprises struggle to extract actionable insights from unstructured data. Manual research takes days, is prone to bias, and can't scale across domains.
Solution
Built a multi-agent system using RAG + LLM orchestration. Specialized AI agents collaborate autonomously to gather, analyze, cross-reference, and synthesize information into comprehensive reports with citations.
90%
Reduction in manual research effort
360°
Multi-perspective analysis
<2min
Full report generation
System Architecture
What I Build
AI Systems for Real-World Problems
Agentic AI Systems
Agentic AI Systems
Multi-agent architectures that autonomously research, analyze, and generate reports. Built with LangGraph, CrewAI, and custom orchestration layers.
RAG & Search Systems
RAG & Search Systems
Production RAG pipelines with vector databases, semantic retrieval, and hallucination guardrails. From document Q&A to enterprise knowledge bases.
ML & Deep Learning
ML & Deep Learning
End-to-end model pipelines from training to deployment. Computer vision, NLP, transfer learning, and model optimization for production workloads.
How I Build Systems
Architect
Design multi-agent architectures, RAG pipelines, and system topologies before writing code. Define agent roles, communication patterns, and failure modes upfront.
Build
Implement with FastAPI, LangGraph, and vector databases. Write testable, modular code with proper error handling, retry logic, and observability from day one.
Deploy
Containerize with Docker, deploy on AWS/Azure, set up CI/CD pipelines, and monitor with Prometheus + Grafana. Production means it works at 2 AM without you.
Typical System Stack
Every project follows the same discipline: define the architecture, build modular components, deploy with monitoring, and iterate based on real-world performance data.
Engineering Insights
Building AI Agents Locally
Building AI Agents Locally
A deep dive into setting up autonomous AI agents on your local machine using LangChain and open-source LLMs.
FastAPI AWS Deployment
FastAPI AWS Deployment
Step-by-step guide to deploying FastAPI applications on AWS with Docker, ECR, and Lambda for production-grade APIs.
YOLOv8 Brain Tumor Detection
YOLOv8 Brain Tumor Detection
Using YOLOv8 for real-time brain tumor detection from MRI scans — training, evaluation, and deployment insights.
Building AI systems that ship to production
I build multi-agent systems, RAG pipelines, and production AI applications. If you need an engineer who can architect, build, and deploy — let's talk.