Backend-Heavy Full-Stack Engineer | Real-Time Systems & Distributed Data

I engineer scalable backend systems and data-intensive full-stack platforms. I specialize in TypeScript, low-latency data streaming (WebSockets), database optimization (PostgreSQL/pgvector), and deploying serverless architectures to the edge.

• Currently architecting real-time streaming platforms and serverless APIs (Cloudflare Workers, Prisma Accelerate, Monorepos).
• Profiling open-source frameworks to resolve CPU bottlenecks and integrating enterprise-grade authentication (OIDC/JWT) at the infrastructure layer.
• Connect with me on LinkedIn or reach out at amritraj4work@gmail.com.

A low-latency trading engine built for concurrent order execution and live portfolio management.

• Tech Stack: TypeScript, Node.js, React, WebSockets, PostgreSQL.
• Engineering Feat: Designed a multiplexed WebSocket streaming architecture that pushes real-time market tick data to the client without blocking the main UI thread. Implemented strict concurrency controls to eliminate race conditions in financial state management.

An enterprise-grade Retrieval-Augmented Generation (RAG) pipeline with autonomous tool calling.

• Tech Stack: Node.js, Next.js, pgvector, Gemini 2.5 Flash.
• Engineering Feat: Engineered a robust prompt-chaining loop, complex data chunking, and vector similarity search that effectively mitigates LLM hallucinations during multi-workspace queries.

A highly scalable, serverless backend for a social journalism platform.

• Tech Stack: Cloudflare Workers, TypeScript, Prisma Accelerate, Zod.
• Engineering Feat: Deployed entirely to the edge with a strict monorepo architecture. Solved edge-to-database connection bottlenecks using Prisma Accelerate and enforced end-to-end type safety with shared Zod validation schemas.

A custom, high-performance 2D game engine built entirely from scratch.

• Tech Stack: C++.
• Engineering Feat: Built to demonstrate strict system-level programming. Optimized memory management and rendering loops to maintain a consistent high frame rate under heavy sprite loads.

A comprehensive data engineering benchmark suite comparing cloud graph databases.

• Tech Stack: Python, Data Pipelines, Graph Databases.
• Engineering Feat: Rigorously compared managed graph database cloud platforms using standardized, heavy-load datasets and complex workloads to analyze performance bottlenecks and optimization thresholds.

A voice-to-voice AI co-pilot for medical consultations.

• Tech Stack: TypeScript, WebSockets, LLMs, Audio Streaming.
• Engineering Feat: Engineered a real-time asynchronous pipeline handling continuous audio streaming, live transcription, and text-to-speech with stringent low-latency constraints.

I actively contribute to distributed orchestration and agentic AI frameworks. I focus on architectural improvements, LLM reliability, and performance bottlenecks.

• aden-hive/hive: Engineered an LLM self-correction loop within the orchestrator, automatically feeding validation errors back to the model to improve tool-calling reliability. View Pull Request
• rivet-dev/actors: Engineered external OIDC/JWT validation inside onAuth lifecycle hooks to support enterprise authentication. View Pull Request
• rivet-dev/actors: Identified and benchmarked a severe O(N) array lookup CPU bottleneck occurring under high load in the runner tunnel. View Architecture Issue