Senior Full-Stack AI/ML Software Engineer

James Britt

713-***-**** *****.*.*****@*******.*** Midland, TX

Professional Summary

Senior Software Engineer with 9 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, and AWS

Technical Skills

FrontEnd React, TypeScript, Angular, Material-UI, TailwindCSS, D3, Rechart BackEnd Node.js, Express, Java, Spring Boot, Python, FastAPI, REST APIs, GraphQL Cloud AWS EC2, ECS, Lambda, Step Functions, EventBridge, API Gateway, Glue, SMS, SQS, SNS DevOps CI/CD pipeline, Git, GitHub Action, Terraform, AWS CDK, Docker, Kubernetes Data & AI/ML SQL / NoSQL, PostgreSQL, DynamoDB, S3, Redis, LLM API, RAG, Pinecone, MCP, Cursor, Claude Other OAuth2, JWT, Okta, Jest, React Testing Library, Playwright, Mocha, Pytest, JUnit Professional Experience

Teladoc Health Senior Software Engineer April 2021 - Present

Built a AI-Powered Health Intelligence platform supporting 1M+ users, enabling patients to ask questions and receive AI-generated insights from ETL-processed RPM and EHR data, using Node.js, Python, React and AWS.

Built the frontend using React, TypeScript, Material-UI, and TailwindCSS, integrating with GraphQL and REST APIs to deliver interactive AI-powered health insights.

Developed REST APIs using Node.js, Express, integrating multiple healthcare data sources and delivering AI-powered insights while enforcing secure, role-based access controls for patients, clinicians, and researchers.

Designed and developed Node.js, Python microservices, building modular services for query processing, AI inference orchestration, data transformation, access control, and response delivery to support event-driven AI-powered health workflows.

Implemented a serverless, event-driven architecture using AWS Lambda, Step Functions, and EventBridge to orchestrate workflows and process AI-powered health queries efficiently.

Implemented authentication and authorization using OAuth2, JWT, and Okta, integrating with API Gateway and Lambda authorizers to enforce role-based access and patient consent, ensuring secure, HIPAA-compliant delivery of health data.

Designed schemas, wrote queries, and ensured data persistence using PostgreSQL with Prisma / SQLAlchemy, managing migrations and enforcing RBAC for secure, scalable user accounts.Implemented Redis caching for frequently accessed telemetry and AI response data.

Implemented DynamoDB for session metadata and backend state, and S3 for storing processed telemetry and reference datasets, enabling fast, reliable, and scalable serverless backend workflows.

Built ETL pipelines for ML training using AWS Lambda, Glue,and S3, performing data extraction, cleaning, normalization, incremental loading, and data quality checks to produce reliable, structured datasets for model training.

Integrated LLM APIs using REST API (Python FastAPI) for sending structured input and receiving inference outputs, and GraphQL for retrieving structured RAG context, ensuring accurate, context-aware AI responses in the production platform.

Built supporting pipelines using RAG, vector DB, and MCP to retrieve, filter, and structure clinical and user-uploaded data, ensuring accurate, compliant, and context-aware inputs for the LLM.

Deployed finetuned LLM (DeepSeek R1) and vision models (MedGemma) on EC2 instances with load balancing, using Terraform to provision, configure, and scale infrastructure for reliable AI inference.

Implemented CI/CD pipelines using GitHub Actions and Terraform (IaC) to automate provisioning and deployment of GPU EC2 instances, load balancers, and serverless microservices, and integrated Datadog monitoring to ensure high- availability and reliable production operations.

Integrated automated testing frameworks (Jest, PyTest, React Testing Library, Playwright) into the development workflow and CI/CD pipeline, ensuring unit, integration, and end-to-end tests ran automatically to maintain reliability and correctness across frontend, backend, and AI workflows.

Leveraged AI tools (Cursor and Claude) for coding and automation, boosting coding productivity by ~90%, accelerating delivery of APIs and TypeScript dashboards.

Walmart Software Engineer June 2019 – March 2021

Developed Marketing Analytics Dashboard that reduced report preparation time by ~30%, enabling marketing teams to make faster, data-driven campaign decisions.

Built Data pipelines using Python, Pandas, Celery and AWS Lambda to collect, clean, and transform marketing data from multiple sources.

Developed and optimized REST APIs with Node.js and PostgreSQL, serving processed data reliably for analysts.

Enhanced dashboards using React and Recharts, deployed in internal Docker environments, delivering actionable visualizations of campaigns and customer engagement. Accenture Software Engineer June 2017 – May 2019

Built and optimized React applications, designing scalable component architecture and reusable UI patterns for enterprise clients.

Managed complex state and integrated REST APIs and GraphQL queries, implementing real-time updates with WebSockets to deliver dynamic, responsive interfaces.

Optimized frontend performance, applying code splitting, lazy loading, memoization, and modular architecture, ensuring production-ready, high-performance applications.

Ensured application quality with automated testing (Jest, React Testing Library) and CI/CD workflows, delivering maintainable and reliable frontend solutions.

Education

University of Texas at Austin Bachelor of Science in Computer Science Aug 2013 – May 2017

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