Open to opportunities

Williams Shwan

@williamsshwan

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Senior AI full-stack engineer building production LLM, RAG, and agentic workflows at scale.

United States

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What I'm looking for

I’m looking to build production-grade GenAI products—LLM apps, RAG, and LangGraph agentic workflows—with strong evaluation, observability, and secure multi-tenant delivery, where reliability, latency, and cost are treated as first-class priorities.

I’m a Senior AI Full Stack Engineer with 10+ years of experience delivering production-grade AI, SaaS, backend, and full-stack applications. I specialize in Large Language Model (LLM) applications—especially Retrieval-Augmented Generation (RAG) and agentic systems.

At Nike and across client work, I’ve built scalable GenAI solutions for both enterprise and startup environments. I design RAG pipelines with hybrid search, reranking, metadata filtering, and retrieval evaluation loops to improve accuracy and reduce hallucinations, then productionize with secure multi-tenant architectures and robust observability.

I also develop LangChain and LangGraph-based multi-agent workflows with tool calling, conditional routing, memory handling, and workflow state management. I care deeply about evaluation, response validation, and reliable deployment—turning promising prototypes into dependable AI-powered products.

Experience

Work history, roles, and key accomplishments

Current

Senior AI Backend Engineer

Current

Nike

Jan 2023 - Present (3 years 4 months)

Designed and deployed production LLM applications using Python, FastAPI, OpenAI/Claude, and AWS to enable intelligent automation and user-facing GenAI features. Built RAG pipelines and LangGraph agentic workflows, integrated enterprise data and internal APIs, and added evaluation and observability for production readiness.

Python fastAPI LangGraph Retrieval Augmented Generation (RAG)Vector Databases AWS PostgreSQL Observability

Full Stack Engineer

Tech Consulting Group

Nov 2020 - Nov 2022 (2 years)

Built full-stack, LLM-powered client solutions including semantic search, content generation, automation workflows, and knowledge assistants. Developed RAG systems and agentic workflows with document indexing, tool/API integrations, multi-tenant security, and production monitoring.

Python fastAPI Retrieval Augmented Generation (RAG)Vector Databases Workflow Agents Observability

Software Engineer

Google

Mar 2018 - Oct 2020 (2 years 7 months)

Developed backend systems for large-scale data processing, internal analytics, experimentation, and distributed workflows. Implemented event-driven pipelines on Google Cloud and improved observability, reliability, and experimentation infrastructure.

BigQuery Pub Sub Cloud Functions Event Driven Systems A B Testing API Design Data Pipelines Observability

Full Stack Developer

Red Ventures

Jan 2017 - Feb 2018 (1 year 1 month)

Delivered full-stack features and backend APIs for high-traffic consumer platforms across marketing, analytics, and digital product workflows. Built microservices, integrated frontend-to-backend APIs, and optimized SQL, caching, and CI/CD deployments for performance and reliability.

Java React REST APIs Microservices SQL Optimization Caching Strategies A B Testing Enablement

Software Engineer

Accenture

Jul 2012 - Dec 2016 (4 years 5 months)

Designed and implemented scalable REST APIs and backend services using Java, Spring Boot, Node.js, and MySQL. Improved database performance and helped transition monolithic systems toward modular, service-oriented architecture with Agile/Scrum delivery and production support.

Java Spring Boot REST APIs MySQL Agile Scrum Production Support