𝙷𝚒 𝚝𝚑𝚎𝚛𝚎 👋, 𝙸’𝚖

Backend Engineer specializing in AI agent orchestration and scalable systems. Currently building production-grade multi-agent pipelines with FastAPI, pgvector RAG, and LLM routing strategies. Experience with serverless architectures, vector databases, and real-time processing.

𝙲𝚞𝚛𝚛𝚎𝚗𝚝 𝚁𝚘𝚕𝚎 : 𝙵𝚘𝚞𝚗𝚍𝚒𝚗𝚐 𝙴𝚗𝚐𝚒𝚗𝚎𝚎𝚛 𝙸𝚗𝚝𝚎𝚛𝚗 @ 𝐌𝐚𝐫𝐤𝐨 𝐀𝐈 | 𝙲𝚂 𝚄𝚗𝚍𝚎𝚛𝚐𝚛𝚊𝚍 '𝟸𝟽

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💡 Core Stack & Technologies

               

🚀 Current Focus

🛠️ Building Production-grade multi-agent systems with LangGraph, FastAPI, and pgvector RAG	⚙️ Working On LLM routing, rate-limit optimization, real-time agent orchestration
📚 Exploring LLM observability and distributed task queues	🎯 Open To Full-time AI/Backend roles (not internships)

🏆 GitHub Trophies 🏆

My GitHub Vitals

My Contribution Dashboard

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📊 Impact & Metrics

   

   

💻 My Tech Arsenal

⚙️ Backend & AI

🗄️ Database & Vector Stores

☁️ Cloud & DevOps

🎨 Frontend & Tools

Featured Projects & Production Work

🤖 Multi-Agent Lead Intelligence Pipeline Built a three-agent pipeline (Researcher → Contact Finder → Outreach Writer) using FastAPI with async orchestration. Processes companies in parallel batches with controlled concurrency, reducing runtime from ~8 minutes to ~4 minutes. Implements LLM routing via Groq to handle rate limits, integrates Tavily for data extraction, and uses structured outputs across agents. Includes retry logic with exponential backoff and fallback handling to ensure stable execution under failures.

🏢 Marko AI - Reflection & Learning Agent Backend Built backend systems for reflection and learning agents using FastAPI with pgvector-based semantic memory. Migrated storage from SQLite + ChromaDB to Supabase PostgreSQL to support scalable vector retrieval and context persistence. Implemented multi-tenant agent scoping with embedding-based isolation and stabilized 8+ API endpoints by resolving schema inconsistencies (UUID vs int). Containerized the backend with Docker and established a consistent local-to-cloud deployment workflow.

🔍 Trace-Ability - Serverless Code Analysis Built a serverless pipeline using AWS Lambda to analyze commit diffs and infer developer intent across multi-file changes. Processes 40+ file diffs per run and stores telemetry in DynamoDB with secure IAM-based access. Optimized inference latency to <3.5s and reduced cost to ~$0.01 per run by migrating to Amazon Nova Lite. Integrated CI/CD checks via GitHub Actions to flag misleading commits using a trust score system.

⏱️ Recent Activity

Apr 2026 – Present	🏢 Marko AI – Founding Engineer Intern Building reflection and learning agents with pgvector-based semantic memory. Stabilized core pipelines (8+ APIs), implemented async orchestration, and improved reliability with validation, fallback logic, and feedback tracking.
Apr 2026	🤖 Multi-Agent Lead Intelligence Pipeline Built and deployed a 3-agent FastAPI pipeline with async batching (3 companies per batch), reducing runtime from ~8 min to ~4 min. Implemented Groq-based LLM routing, Tavily integration, and retry-based fault tolerance.
Mar 2026	🔍 Trace-Ability + 🌟 Open Source Built AWS Lambda pipeline to analyze commit diffs across 40+ files per run with <3.5s latency and ~$0.01 cost. Contributed to scancode.io and submitted GSSoC proposals.
Jan 2026 – Feb 2026	🛡️ EduShield AI Built a full-stack FastAPI + React system for phishing email analysis with asynchronous backend processing. Implemented TF-IDF + Scikit-learn detection with rule-based heuristics and ensured local-first inference with SHA-256 hashing.
Sep 2025 – Nov 2025	⚙️ Algorithmic Assessment Engine Built FastAPI backend for evaluating code submissions with deterministic scoring, JWT-based authentication, and MySQL schema design.

🎯 Closing Note

Focus: Building reliable backend systems for AI agents — orchestration, vector memory, and structured LLM pipelines

Approach: Prioritize determinism, fault tolerance, and performance over hype-driven implementations