Senior Full-Stack AI Software Engineer

James Brittan Senior Software Engineer with * years of experience in developing value-driven software solutions. Focused on full-stack engineering, data engineering and AI/ML (GenAI solution) integration into production. Skilled in Node.js, Python, React, SQL/NoSQL and AWS.

512-***-**** Huntsville, TX *****.****@*******.*** Skills

FrontEnd HTML, CSS, ES6+, React, Next.js, Material UI, Bootstrap, TailwindCSS, D3, Rechart BackEnd Node.js, Express, Nest.js, Fastify, Middy.js, Langchain.js, Python, Django, Celery, REST APIs, GraphQL Cloud AWS EC2, ECS, Lambda, Step Functions, EventBridge, API Gateway, Glue, SMS, SQS, SNS DevOps CI/CD pipelines, Git, GitHub Actions, Jenkins, Terraform, AWS CDK, Docker, Kubernetes DB & AI/LLM SQL / NoSQL, PostgreSQL, DynamoDB, MongoDB, S3, Redis, ETL, GenAI, RAG, Pinecone, Claude, Cursor, Codex Other OAuth2, JWT, Okta, Jest, React Testing Library, Playwright, Mocha, Pytest, JUnit Professional Experience

Hims & Hers San Francisco, CA

Senior Software Engineer Jan 2022 – Present

Built an AI-powered Clinical Intelligence Platform, enabling clinicians and patients to interact with EHR data through AI-driven insights and RAG-based retrieval workflows, using Node.js, Python Django and React.

Developed responsive frontend using React, TypeScript, and Material UI/TailwindCSS, delivering interactive dashboards for clinical document upload, AI query execution, and response visualization.

Designed and implemented RESTful and GraphQL APIs using Node.js (Express/NestJS) and Python Django, enabling secure communication between frontend, AI Gateway, and backend clinical services.

Built modular Node.js + Python microservices for AI orchestration, query processing, document ingestion, and retrieval services, enabling scalable separation of concerns across clinical AI workflows.

Implemented a serverless architecture using AWS Lambda (Middy.js), API Gateway, Step Functions, and S3 for document ingestion, event-driven processing, and asynchronous AI workflow execution.

Designed and implemented event-driven pipelines using AWS EventBridge and SQS with Celery, enabling real-time processing of clinical document updates, embedding generation, and AI query triggers.

Implemented secure authentication and authorization using OAuth2, JWT, and Okta, with RBAC enforcing role-based access for clinicians, patients, and researchers while ensuring HIPAA-aligned data access control.

Designed data persistence layer using PostgreSQL + TypeORM/Django ORM for structured clinical data, S3 for document storage, and Redis caching to optimize retrieval latency for frequently accessed AI responses and telemetry data.

Built MongoDB (NoSQL)-backed services to store document metadata, chat history, and AI processing data for a clinical document assistant.

Built a scalable ETL pipeline using Databricks (Spark) and Delta Lake on S3 to ingest, transform, and standardize large-scale EHR and clinical data into versioned datasets for analytics and AI workflows.

Integrated LLM APIs using structured prompts and API-based inference via Open AI’s GPT-4, enabling AI-generated clinical insights.

Implemented RAG pipeline (LangChain) combining vector embeddings with clinical document search using a vector db (Pinecone) to ensure grounded, context-aware AI responses based on patient-specific data.

Migrated core AI Gateway services from AWS serverless to containerized microservices using Docker and ECS/EC2, improving latency, scalability, and control over long-running AI inference workflows.

Established CI/CD pipelines using GitHub Actions/Jenkins, Infrastructure-as-Code using AWS CDK/Terraform, and monitoring with CloudWatch, Prometheus, and Grafana for observability of AI workflows and system health.

Implemented automated testing strategies including unit, integration, and E2E testing using Vitest, Jest, Pytest, React Testing Library, and Cypress, integrated into CI pipelines to ensure reliability of AI and clinical workflows.

Leveraged AI-assisted development tools such as Cursor, Claude Code, and Codex-based copilots to accelerate backend API development, prompt engineering, and frontend implementation, improving engineering productivity and iteration speed. Walmart, Inc Bentonville, AR

Software Engineer Jan 2020 – Dec 2021

Built a Marketing Analytics Dashboard that reduced reporting time by ~20%, enabling faster, data-driven campaign optimization for marketing teams.

Designed and implemented Data Pipelines (Python, Pandas, Celery, AWS Lambda) to ingest, clean, and transform multi-source marketing data at scale.

Developed and optimized RESTful APIs (Node.js, PostgreSQL) to serve processed analytics data with high reliability for downstream reporting and analysis.

Built interactive dashboards using React and Recharts, containerized with Docker for internal deployment, delivering actionable insights on campaign performance and customer engagement. Commure, Inc Mountain View, CA

Software Engineer Jun 2017 – Dec 2019

Built scalable Next.js/React frontend applications for enterprise use cases, implementing modular and reusable component architectures to improve maintainability and development efficiency.

Integrated REST and GraphQL APIs with advanced state management and real-time updates via WebSockets, enabling dynamic, data- driven user experiences.

Improved frontend performance through server-side rendering (SSR), code splitting, lazy loading, and memoization, delivering fast, production-optimized web applications.

Ensured application reliability and maintainability by implementing automated testing (Cypress, React Testing Library) and CI/CD pipelines for consistent, production-grade deployments. Education

University of Texas at Austin Austin, TX

Bachelor of Science (B.S) in Computer Science, GPA 3.8 Aug 2013 – May 2017

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