I'm a full-stack developer and AI engineer with an Electronics & Communications Engineering background and a bias toward shipping real products. I enjoy working across the entire stack: product thinking, system design, type-safe APIs, data modeling, cloud infrastructure, and AI orchestration.

My work focuses on making LLM features useful in production - retrieval quality, latency, rate limiting, graceful failure, observability, and clear user experiences matter as much as the prompt.

A live developer community platform built and maintained as an independent product.

• Community feeds, nested discussions, rich-text publishing, product launches, voting, bookmarks, and job discovery.
• AI assistant with local embeddings (Xenova/Transformers), Upstash Vector retrieval, Groq inference, and custom multi-agent orchestration (Router → Semantic Search → Guardrail agents) — built without LangGraph for zero-dependency control.
• Reddit market intelligence pipeline that extracts demand signals, competitor insights, monetization ideas, and go-to-market recommendations.
• Production infrastructure spanning Next.js, TypeScript, Prisma, MySQL, Upstash Redis, Stripe, PostHog, and GitHub Actions.
• Built with attention to caching, rate limiting, type safety, security, and resilient third-party integrations.

A collaborative travel-planning system that decomposes a complex request across specialized agents.

• LangGraph state graph with parallel flight, hotel, and itinerary agents.
• Shared agent state and PostgreSQL persistence for resumable planning sessions.
• Streamlit interface for interactive planning and review.
• Designed around separation of concerns, deterministic orchestration, and recoverable workflows.

An automated opportunity-discovery system for SaaS and developer products.

• Collects and processes discussions from relevant Reddit communities.
• Uses LLM extraction to identify pain points, demand strength, competitors, pricing, and launch strategy.
• Combines caching, rate limiting, structured outputs, and human-readable reports.
• Demonstrates an end-to-end pipeline from raw community data to actionable product insight.

![Lalit's GitHub stats](`/api?username=lalitdotdev&show_icons=true&hide_border=true&count_private=true&include_all_commits=true&theme=transparent)

flowchart LR
    A[User problem] --> B[Define success criteria]
    B --> C[Data & retrieval strategy]
    C --> D[Agent / model workflow]
    D --> E[Tools & APIs]
    E --> F[Guardrails & evaluation]
    F --> G[Streaming UX]
    G --> H[Observability & iteration]
    H --> B

I start with the user outcome, not the model. Then I design the smallest reliable system that can reach it — with explicit failure modes, measurable quality, and a path to improve over time.

• AWS Certified — Cloud Technical Essentials, Coursera (98%)
• Continuous learning across distributed systems, applied AI, product engineering, and cloud architecture

I'm interested in conversations about:

• AI engineering and applied LLM product development
• RAG, evaluation, and multi-agent systems
• Full-stack product engineering
• Developer tools and community platforms
• Turning ambiguous ideas into shipped, maintainable software