Software Engineer focused on backend systems, AI infrastructure, distributed systems, and performance engineering.

I build scalable software, developer tooling, and AI-powered systems using TypeScript, Node.js, React, Redis, AWS, and modern LLM frameworks. My interests span production backend engineering, AI agents, retrieval-augmented generation (RAG), evaluation pipelines, and infrastructure for reliable AI applications.

Currently exploring local LLM inference, agent architectures, evaluation-driven development, and LLMOps while building production-oriented AI systems.

Backend & Distributed Systems

Frontend

Databases & Cloud

Developer Tools & Quality

Languages & Foundations

Backend Systems • Distributed Systems • AI Infrastructure • LLMOps • Agentic AI • Retrieval-Augmented Generation (RAG) • Performance Engineering • Developer Tooling • Observability

• Reduced production JavaScript bundle size by 62% (893KB → 337KB) through dependency optimization, route-level code splitting, and lazy loading

• Achieved 95+ Lighthouse Performance and 100 Best Practices

• Built automated resume parsing workflows leveraging PDF.js's Web Workers, reducing manual data entry effort by 70%

• Designed async job processing pipelines using Redis and BullMQ

• Developed audit logging systems with indexed querying and paginated retrieval APIs

• Engineered WhatsApp-based candidate sharing workflows using AWS S3, CloudFront, Redis queues, and the WhatsApp Business API

• Implemented CI/CD workflows and maintained 80%+ test coverage using GitHub Actions, Vitest, Jest, and React Testing Library

• Built PerfEngine, a static performance regression detection engine for JavaScript and TypeScript pull requests

• Local LLM inference
• AI infrastructure
• Agent architectures
• LLMOps
• Evaluation-driven development
• Long-term memory systems

I write about backend engineering, distributed systems, performance engineering, production debugging, AI infrastructure, and system design. Recent Articles

• Designing a Production-Grade Async Job Queue with Redis and BullMQ
• Lessons from Building a Production PDF Parser with Web Workers
• Memory Profiling a Production React App
• Dual Storage for Time: Designing Timezone-Safe Systems at Scale
• Debugging a Silent Validation Failure in Production
• How I Reduced Our React Bundle by 62%

➡️ Full blog: https://jaivalsuthar.hashnode.dev

Technical Leadership

• Executive Committee Member, IEEE Signal Processing Society
• Core Team Member, Google Developer Student Clubs (GDG), Silver Oak University

Continuous learner with a deep interest in software architecture, distributed systems, AI infrastructure, and the engineering principles behind production-scale AI systems.

Open to discussions around: Backend Engineering, Distributed Systems, AI Infrastructure, LLMOps, Retrieval-Augmented Generation (RAG), AI Agents, System Design, Performance Engineering, and Developer Tooling.

Interested in high-impact engineering problems, strong ownership, and systems that matter.