DISTRIBUTED SYSTEMS · DEFI INFRASTRUCTURE · AGENTIC AI

Systems That
Scale, Settle,
& Survive.

Senior architect shipping production-grade distributed backends, multi-chain DeFi infrastructure, and agentic AI pipelines. I design for partition tolerance, compensate failures automatically, and scale horizontally — because vertical limits exist.

4+ Years Production-Grade Systems
p95 Latency Improvement
3 Blockchain Networks (EVM, SOL, BTC)
0 Financial Leakage Across All Sagas
CHAPTER ONE · INTRODUCTION

Building distributed backends where correctness is enforced, failures are compensated automatically, and scale is horizontal by default.

I’m Ritesh Khamitkar — a distributed systems and blockchain architect with 4+ years shipping production infrastructure at Jio Platforms, two funded US/Singapore-based startups, and solo. My work sits at the rare intersection of three domains: distributed backend systems, multi-chain DeFi infrastructure, and production agentic AI.

I design around CAP theorem trade-offs — strong consistency where money moves on-chain, eventual where throughput demands it. Every service I ship uses saga orchestration for distributed transactions, idempotency keys at every execution boundary, and the outbox pattern for guaranteed at-least-once delivery. CQRS separates read and write paths before they become bottlenecks. When failures hit, circuit breakers trip, compensating transactions roll back cleanly, and self-healing reconciliation daemons resolve state drift without waking anyone at 3am.

On-chain: raw Bitcoin PSBT construction → direct mempool broadcast, Solidity escrows with keccak256 commit-reveal on Base L2, gasless EVM/Solana paymasters. On the AI side: multi-agent pipelines on Vertex AI with 4× p95 latency improvements through async parallelization, Firestore caching, and connection pooling. The terminal below is live proof — not slides.

guest@ritesh-system:~
> curl -s https://api.ritesh.dev/status | jq .
{ "name": "Ritesh Khamitkar", "title": "Distributed Systems & Blockchain Architect", "experience": "4+ years · Production-Grade", "distributed_systems": { "patterns": ["Saga+Compensation", "CQRS", "Outbox", "Event Sourcing", "Circuit Breaker"], "consistency": "Strong where money moves. Eventual where throughput demands.", "delivery": "At-least-once + Idempotency keys at every boundary" }, "blockchain": ["EVM / Base L2", "Solana", "Bitcoin PSBT", "Smart Contracts"], "backend": ["NestJS", "FastAPI", "Kafka", "PostgreSQL", "gRPC", "Redis"], "ai": ["Google ADK", "MCP", "Vertex AI Reasoning Engine", "RAG Pipelines"], "infra": ["AWS (EC2/RDS/S3)", "GCP Cloud Run", "Docker", "k8s", "GitHub Actions"], "p95_latency_improvement": "4× (asyncio + Firestore cache + connection pooling)", "fault_tolerance": "Circuit Breakers · Compensating Txns · Zero SPOF", "status": "available_for_technical_evaluation" }
> node saga_engine.js --execute-swap

// Distributed Multi-Chain Swap Saga Simulator (NestJS logic)

IDEMP_CHECK
INITIATED
ESCROWED
SOL_RPC_FAIL
ROLLED_BACK
// State Machine idle. Click "Run" to test distributed transaction saga.
> python reconcile_daemon.py --detect-drift

// Self-Healing Ledger Reconciliation & Finality Daemon (PostgreSQL + Base L2)

SCAN_POSTGRES
STUCK_DETECTED
QUERY_CHAIN_RPC
CONFIRMATIONS (0/12)
RECONCILED_SETTLED
// Daemon sleeping. Click "Trigger" to simulate post-restart database recovery.
SYSTEMS DNA · THE COMPENDIUM

Capabilities

BLOCKCHAIN & SMART CONTRACTS
  • Solidity / Hardhat / Foundry
  • Gas Optimization / CREATE2
  • ERC-20 / ERC-721 / OpenZeppelin
  • Base (L2) / zkSync / Multi-chain
  • EVM & Solana Paymasters
  • Liquid Staking / LI.FI Cross-Chain
WEB3 INFRASTRUCTURE
  • Turnkey SDK (Passkeys / Policies)
  • Privy / Coinbase CDP / X402
  • Bitcoin PSBT / direct Mempool
  • Bridge.xyz On/Off-Ramps
  • Alchemy RPC / ethers.js
AGENTIC AI & DATA
  • Google ADK / Vertex AI Reasoning
  • Model Context Protocol (MCP)
  • Mem0 Vector Memory / LlamaIndex
  • Pinecone / Neo4j Knowledge Graphs
  • Text-to-SQL refinement models
BACKEND & DEVOPS
  • NestJS / FastAPI / TypeScript / Python
  • Kafka Streaming / Redis / gRPC
  • Docker / Kubernetes / Helm
  • GCP Cloud Run / AWS (EC2/RDS)
  • GitHub Actions CI/CD / PM2
DISTRIBUTED SYSTEMS DESIGN
  • CAP / PACELC · Consistency Models
  • Saga Pattern · Compensating Transactions
  • CQRS · Event Sourcing · Outbox Pattern
  • Idempotency · Exactly-Once Semantics
  • Horizontal Scaling · Sharding · Replication
  • Circuit Breaker · Backpressure · Rate Limiting
  • Leader Election · Quorum · Gossip Protocols
  • Distributed Transactions (2PC · Sagas)
INTELLECT & HORIZON

Education

M.S. Artificial Intelligence
BITS Pilani
Apr 2023 — Mar 2025 · India
B.E. Information Technology
Pune University
Jun 2019 — May 2023 · India
CASE 01 · FOUNDING ENGINEER (BLOCKCHAIN & BACKEND INFRASTRUCTURE)

Avvio Neobank

Nov 2025 — Present · Remote (US)
  • Designed and built the entire backend architecture from scratch — modular NestJS Service–Handler pattern with bounded contexts per chain domain (EVM, Solana, Bitcoin); stateless microservices behind an API gateway, horizontally scalable with zero shared mutable state across service boundaries.
  • Engineered gasless transaction infrastructure across EVM and Solana (Turnkey Gas Station, paymasters, Alchemy RPC, ethers.js, LI.FI); implemented idempotency keys and retry-with-jitter on all RPC calls — eliminating duplicate broadcasts and ensuring at-least-once delivery with deduplication at the handler layer.
  • Developed, tested, and deployed AvvioPaymentLinkV2 Solidity smart contract on Base L2 — dual escrow flows (Request & Send Links), keccak256 commit-reveal claim secrets, CREATE2 deterministic deployment via Hardhat, with OpenZeppelin ReentrancyGuard, Pausable, and SafeERC20.
  • Built @username transfer router that auto-selects the lowest-fee chain (EVM vs. Solana) for multi-chain USDC — and native Bitcoin module: PSBT construction, Turnkey signing, direct mempool broadcast with no bridge dependencies; implemented Push Notification/Email/SMS backend for iOS and Android.
  • Integrated Bridge.xyz fiat on/off-ramp (ACH, SEPA, CLABE, PIX across USD/EUR/MXN/BRL/GBP) and Sumsub KYC with partner-agnostic PII architecture; deployed Turnkey embedded business wallets with multi-user org RBAC and on-chain spending-limit policies via Turnkey Policy Engine.
  • Deployed on AWS (EC2, RDS PostgreSQL, S3, Secrets Manager, CloudTrail); established GitHub Actions CI/CD with multi-stage Dockerfile, health-check endpoint, SSH tunnel auth, and PM2 process management; enforced secret rotation and zero plaintext credentials in runtime environment.
CASE 02 · FOUNDING ENGINEER (SENIOR AI & WEB3 BACKEND)

Qurios AI

Jan 2025 — Oct 2025 · Remote (Singapore)
  • Architected production agentic AI backend: multi-agent orchestration with Google ADK, MCP server, and Mem0 vector memory; deployed Vertex AI Reasoning Engine agents on GCP Cloud Run with horizontal autoscaling and graceful shutdown on SIGTERM — no cold-start latency spikes.
  • Designed multi-stage RAG retrieval pipelines (LlamaIndex, Pydantic, async ThreadPoolExecutor) with parallel LLM calls; cut API p95 latency 4× via asyncio parallelization, Firestore read caching with TTL invalidation, and connection pool sizing tuned to concurrency profile.
  • Built high-throughput NestJS microservices with Kafka consumer groups ingesting multimodal streams (Twitter, Telegram, YouTube, Substack, PDFs, audio) for automated AI personality creation; dead-letter queues for poison messages; CRON-based ETL and gRPC (BloomRPC) inter-service communication.
  • Integrated X402 protocol with Coinbase CDP Facilitator for on-chain micropayments; deployed Base MiniApp end-to-end with LlamaPay subscription streaming, webhook listeners, and Privy wallet auth; built Neo4j knowledge graphs with Pinecone vector embeddings for contextual retrieval and Text-to-SQL refinement models.
CASE 03 · DEVOPS & AI/ML ENGINEER

Jio Platforms Limited (JPL)

May 2023 — Dec 2024 · Navi Mumbai, India
  • Architected production AI backend for Jio Brain — multi-agent orchestration with FastAPI, RAG pipelines (Pinecone vector memory + Neo4j knowledge graphs), and distributed anomaly detection at enterprise telecom scale; designed for horizontal scaling with stateless Cloud Run services and Kafka-backed event ingestion.
  • Built high-throughput Kafka ingestion microservices with consumer group offset management, CRON-based ETL pipelines, async Cloud Run batch jobs, and gRPC inter-service communication; implemented dead-letter queues for poison-message isolation and CRON reconciliation for idempotent re-processing.
  • Developed production RESTful APIs with OAuth2 authentication and HashiCorp Vault secret management; automated MLOps using Jenkins, Helm, and Kubernetes (rolling deployments, liveness/readiness probes, zero-downtime updates) for continuous model deployment pipelines.
  • Implemented distributed model training on GCP with TensorFlow Extended (TFX) and Kubeflow; applied Bayesian Optimization and Grid Search hyperparameter tuning; reduced API p95 latency 4× through async parallelization, Firestore caching, and targeted connection pool configuration.
ACTIVE PROOF · DEPLOYMENTS

Independent Research & Deployments

DEX Automation & Layer 2 Trading Systems

Python · FastAPI · Hyperliquid · Paradex

Engineered production DEX automation bots executing multi-wallet trading strategies, yield farming, and cross-chain arbitrage on Layer 2 with real-time price feeds, slippage protection, mempool monitoring, and MEV protection.

Agents.surf — AI Agent Discovery Platform

GCP Cloud Run · Docker · Full-Stack

Architected and deployed an AI agent discovery platform with intelligent search indexing, agent categorization taxonomy, and real-time product feed ingestion; currently serving 60+ daily active users.

PRE-ENGAGEMENT TECHNICAL EVALUATION

No-Risk Technical Trial

Available to architect and ship one production-grade service, smart contract, or AI agent integration as a no-risk technical evaluation (1–2 weeks) before any formal commitment — zero obligation for the founding team. I can give a live demonstration of execution speed and code quality on a real feature from your backlog, with production-grade system design end to end.