Parbhat Kapila
Full-Stack Engineer
AI • Backend Systems
Parbhat Kapila
Full-Stack Engineer
AI • Backend Systems
Parbhat Kapila
System Architect
Engineering at Scale
Why I Build
I'm Parbhat, a full-stack engineer specializing in building high-performance, scalable applications for fast-moving startups. With expertise spanning distributed systems, AI integration, and modern web architectures, I architect end-to-end solutions that handle users while maintaining clean, maintainable codebases. I excel at transforming complex business requirements into production-ready systems, leading technical initiatives, and mentoring teams through challenging engineering problems.
Present
Currently architecting enterprise-grade AI collaboration platforms that transform meeting data into actionable intelligence at scale. Leading the design and implementation of distributed real-time systems processing thousands of concurrent sessions, with RAG-based semantic search, multi-model AI orchestration, and event-driven microservices architecture. Building resilient infrastructure with automated failover, horizontal scaling, and trying to achieve sub-100ms response times—delivering solutions that reduce operational overhead by 10x while maintaining 99.99% uptime.
Technical Expertise
Featured Projects
Enterprise email intelligence platform leveraging advanced RAG architecture and multi-model AI orchestration (GPT-4, Claude, Gemini) to deliver context-aware email automation. Engineered distributed vector embedding pipeline with Redis clustering, processing 100's of emails with low search latency. Implemented adaptive learning system using transfer learning on user communication patterns, improving response accuracy by 40%. Architected serverless edge deployment achieving 70% cold start reduction and 99.9% availability. Integrated Stripe subscription infrastructure with idempotent webhook processing and automated billing reconciliation.
AI-powered developer productivity platform that reduces onboarding time by 75% through intelligent codebase documentation and semantic code search. Architected distributed processing pipeline handling heavy repositories with parallel AST parsing, incremental indexing, and smart chunking strategies. Built hybrid search engine combining pgvector similarity with BM25 keyword matching, achieving 92% relevance accuracy. Designed serverless event-driven architecture with SQS queuing, Lambda workers, and DynamoDB state management for concurrent processing of repositories. Implemented multi-tenant data isolation with row-level security and automated cache invalidation.
Enterprise document intelligence SaaS reducing document analysis time from hours to seconds through advanced LLM orchestration. Engineered sophisticated extraction pipeline with LangChain agents, handling 1000+ page documents through hierarchical map-reduce summarization and intelligent chunking within token constraints. Architected multi-document knowledge graph with cross-reference resolution and temporal context preservation. Built high-performance vector search with HNSW indexing on pgvector, achieving low query response time for embeddings. Implemented robust payment infrastructure with Stripe webhooks, subscription lifecycle management, usage-based metering, and automated invoice generation. Scaled to process documents with 99.9% accuracy.
Experience
Freelance
Full-Stack Developer
Architected and delivered enterprise-grade applications serving users across fintech, SaaS, and AI sectors. Led end-to-end system design for distributed architectures handling 1M+ daily requests with sub-200ms latency {In JP Morgan virtual simulation, we have 100K+ daily requests}. Engineered microservices ecosystems with event-driven patterns, implementing CQRS, saga patterns, and API gateway architectures. Built real-time data pipelines with Redis streams and PostgreSQL, optimizing query performance by 85% through strategic indexing and caching strategies. Designed and deployed CI/CD infrastructure on AWS/Vercel achieving zero-downtime deployments, comprehensive observability with DataDog/Sentry, and 99.95% uptime SLA. Mentored colleagues developers and established coding standards, architectural patterns, and best practices across multiple client teams.