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Hi, I’m Julian Wong. I’m an AI/Software Engineer with 8+ years of experience building full-stack and distributed systems across AWS, fintech, and enterprise SaaS. I design and ship backend services in Python, Java, Spring Boot, Django, and FastAPI, plus frontend apps with React, Angular, and TypeScript. I’m now focused on production-grade Generative AI systems, crafting industry-specific RAG architectures and LLM orchestration workflows that improve task automation and response quality at scale.…Hi, I’m Julian Wong. I’m an AI/Software Engineer with 8+ years of experience building full-stack and distributed systems across AWS, fintech, and enterprise SaaS. I design and ship backend services in Python, Java, Spring Boot, Django, and FastAPI, plus frontend apps with React, Angular, and TypeScript. I’m now focused on production-grade Generative AI systems, crafting industry-specific RAG architectures and LLM orchestration workflows that improve task automation and response quality at scale.

Julian Wong

AI Engineer, Full Stack Developer, Back-End Developer, +4

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Hi, I’m Julian Wong. I’m an AI/Software Engineer with 8+ years of experience building full-stack and distributed systems across AWS, fintech, and enterprise SaaS. I design and ship backend services in Python, Java, Spring Boot, Django, and FastAPI, plus frontend apps with React, Angular, and TypeScript. I’m now focused on production-grade Generative AI systems, crafting industry-specific RAG architectures and LLM orchestration workflows that improve task automation and response quality at scale.…Hi, I’m Julian Wong. I’m an AI/Software Engineer with 8+ years of experience building full-stack and distributed systems across AWS, fintech, and enterprise SaaS. I design and ship backend services in Python, Java, Spring Boot, Django, and FastAPI, plus frontend apps with React, Angular, and TypeScript. I’m now focused on production-grade Generative AI systems, crafting industry-specific RAG architectures and LLM orchestration workflows that improve task automation and response quality at scale.

Available to hire

Hi, I’m Julian Wong. I’m an AI/Software Engineer with 8+ years of experience building full-stack and distributed systems across AWS, fintech, and enterprise SaaS.

I design and ship backend services in Python, Java, Spring Boot, Django, and FastAPI, plus frontend apps with React, Angular, and TypeScript. I’m now focused on production-grade Generative AI systems, crafting industry-specific RAG architectures and LLM orchestration workflows that improve task automation and response quality at scale.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

Javanese

Advanced

Amharic

Intermediate

Bashkir

Advanced

Afar

Intermediate

Work Experience

Software Engineer at Agentic AI Accord

April 1, 2025 - Present

Led AI-assisted revenue operations workflows for B2B SaaS customers using OpenAI API and HuggingFace models, reducing manual CRM updates by 38%. Designed industry-tailored RAG architectures and LLM orchestration for enterprise sales docs, improving domain-specific answer relevance by 29%. Orchestrated multi-step LLM workflows across proposal drafting and customer follow-up processes. Implemented prompt evaluation and observability using LangSmith and MLflow, reducing hallucination incidents by 26% through iterative testing. Built embedding and inference pipelines with Python, Pandas, and Kafka, processing 500K+ customer interactions for semantic indexing. Experimented with lightweight fine-tuning using PyTorch and HuggingFace Transformers to boost domain accuracy by 17%. Deployed scalable LLM inference endpoints using FastAPI, Docker, Kubernetes, and vLLM, maintaining sub-250 ms response latency under concurrent user load.

Software Engineer at Veza

August 1, 2022 - November 1, 2024

Modeled enterprise authorization graphs using Neo4j, Python, and Java, processing 50M+ access relationships to support identity visibility workflows. Orchestrated streaming and batch ingestion pipelines with Kafka, Spark, and Airflow, increasing data synchronization throughput by 41% across multi-tenant environments. Established data normalization layers with Pandas and Python, improving schema validation accuracy for enterprise connectors. Built backend services with Python (Django, FastAPI) and Ruby on Rails, accelerating feature delivery by 27% through modular service boundaries. Implemented role-similarity scoring prototypes with Scikit-Learn for security teams, reducing scanning time on 10K+ identities. Provisioned containerized workloads with Docker and Kubernetes, reducing staging release rollbacks by 23%. Introduced semantically-driven search experiments using HuggingFace embeddings and vector indexing to cut investigation query time by 35%. Consolidated monitoring and audit pi

Software Engineer at Robinhood

June 1, 2021 - August 1, 2022

Delivered high-performance trading interfaces using React and TypeScript, supporting 1M+ daily active users during peak market sessions. Integrated real-time market feeds via WebSocket streams and optimized client-side state handling, reducing UI re-render latency by 37%. Configured CI/CD workflows with GitHub Actions and Docker-based builds, shortening frontend deployment cycles from 45 minutes to 18 minutes. Enhanced data fetch architecture with React Query, eliminating 42% of redundant API requests across portfolio and order views. Improved production observability with DataDog and Sentry, cutting mean time to detect frontend incidents by 33%. Coordinated API schema updates with backend services through contract validation and feature flag rollouts, reducing production regression rates by 24%.

Software Engineer at Amazon Web Services (AWS)

January 1, 2017 - June 1, 2021

Architected distributed backend services in Java and Python powering internal AWS control-plane workflows across 15K+ daily service operations. Designed RESTful APIs using Java, Spring Boot, and AWS SDKs, reducing client integration time by 28% through standardized request validation and schema enforcement. Implemented asynchronous processing pipelines using SQS, Lambda, and EC2, improving job throughput by 34% under peak load. Optimized relational and NoSQL data access across RDS and DynamoDB, reducing query latency from 220 ms to 140 ms for high-volume service calls. Automated infrastructure provisioning via CloudFormation, cutting environment setup time from 3 hours to under 40 minutes. Refactored legacy service modules in Java and C, decreasing memory footprint by 18% and stabilizing performance across large batch workloads.

Full Stack Web Engineer at Robinhood

June 1, 2021 - August 1, 2022

Delivered high-performance trading interfaces using React.js and TypeScript, supporting 1M+ daily active users during peak market sessions. Integrated real-time market feeds via WebSocket streams and optimized client-side state handling, reducing UI re-render latency by 37%. Configured CI/CD with GitHub Actions and Docker-based builds, shortening frontend release cycles from 45 minutes to 18 minutes. Enhanced data-fetching with React Query, eliminating 42% of redundant API requests across portfolios and order views. Strengthened production observability with DataDog and Sentry, reducing mean time to detect frontend incidents by 33%. Coordinated API schema updates with backend services via contract validation and feature flag rollouts, lowering production regression rates by 24%.

Full-Stack Web Engineer at Robinhood

June 1, 2021 - August 1, 2022

Delivered high-performance trading interfaces using React.js and TypeScript, supporting 1M+ daily active users during peak market sessions. Integrated real-time market feeds through WebSocket streams and client-side state handling, decreasing UI re-render latency by 37%. Configured CI/CD workflows with GitHub Actions and Docker-based builds, shortening frontend deployment cycles from 45 minutes to 18 minutes. Enhanced data-fetch architecture with React Query, eliminating 42% of redundant API requests across portfolio and order views. Strengthened production observability using DataDog and Sentry, reducing mean time to detect frontend incidents by 33%. Coordinated API schema updates with backend services through contract validation and feature flag rollouts, lowering production regression rates by 24%.