Alexander Weaver Senior Software Engineer
Summary

Senior backend and platform engineer with deep expertise in AI-driven distributed systems, large-scale data pipelines, real-time runtimes, and high‑performance backend architecture. I design and build scalable, resilient, and secure systems that power AI agents, multimodal pipelines, and mission‑critical analytics workloads. My experience spans LLM orchestration, microVM execution environments, REST API design, cloud infrastructure, and data processing systems across healthcare, real estate, commerce, and enterprise environments. I thrive in early-stage, high‑ownership roles where backend engineering directly shapes product direction and customer impact.

Skills
Programming Languages

JavaScript, TypeScript, Python, Go, Java

Backend & Platform Engineering

Node.js, Go, Python (FastAPI), Elixir, Phoenix, Absinthe, REST APIs, GraphQL, service-oriented architecture, backend scalability, performance optimization, HIPAA-compliant backend systems, RBAC, encryption, audit logging, serverless runtimes, microVM execution environments

AI / Machine Learning & GenAI Systems

OpenAI, Claude, Gemini, Gemini Enterprise, DeepSeek, LangChain, LangGraph, LangSmith, RAG pipelines, AI agents, multimodal systems (STT → LLM → TTS), real-time conversational AI, hybrid search, AI observability

Cloud, DevOps & Reliability Engineering

AWS, GCP, Azure, Docker, Kubernetes, Terraform, Vercel, GitHub Actions, CircleCI, Jenkins, OpenTelemetry, Prometheus, logging & monitoring systems, serverless compute (Lambda), microVM orchestration, Vertex AI Pipelines, Vertex AI Vector Search, Vertex AI Monitoring

Frontend Architecture

React, Next.js, TypeScript, Redux, Zustand, React Query, WebRTC UI flows, component-driven architecture, Storybook, Tailwind CSS, Material UI, SCSS, performance optimization (code splitting, lazy loading, bundle analysis), real-time browser communication (WebSockets, WebRTC)

System Design & Architecture

Distributed systems, scalable system design, event-driven architecture, microservices, real-time systems, multimodal AI pipelines, secure sandboxed runtimes (v8 isolates, Firecracker microVMs), API design, system integration, authentication & authorization, caching strategies, message-driven architectures (Kafka), high-availability systems, retrieval-grounded workflows

Data Systems

PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, pgvector, Pinecone, FAISS, real-time data synchronization, distributed caching, event-driven data pipelines

Experience
MaxHome.AI, Senior Software Engineer
01/2025 – 03/2026 | Richardson, TX
  • •Built React/Next.js front‑end experiences for personalization, zero‑party data capture, guided user flows, and real‑time conversational interfaces using WebRTC, RTP timing, and multimodal interaction.
  • •Developed Java microservices powering recommendation logic, event tracking, user‑intent modeling, and high‑volume data pipelines supporting AI‑powered prospect experiences.
  • •Implemented Terraform‑based DevOps automation and CI/CD pipelines, improving deployment reliability, environment consistency, and autonomous delivery workflows.
  • •Created AI‑powered agentic experiences that captured user intent, answered questions, and guided users through complex decision paths using LLMs, tool‑calling, and structured extraction.
  • •Engineered backend, runtime, and agent‑orchestration layers using TypeScript, PostgreSQL, event‑driven pipelines, encryption, LangGraph, and LangSmith to support reliable AI automation.
  • •Designed autonomous delivery systems where AI generated transformation code, executed it inside secure sandboxes, and validated outputs with guardrails to prevent silent failures.
  • •Built a dual‑runtime sandbox system combining a fast serverless JavaScript runtime (V8 isolates, ~20 ms startup) with a Firecracker‑powered microVM (~2 s cold boot) for secure Python/TypeScript execution.
  • •Integrated Gemini Enterprise and Claude Code into real‑time conversational, property‑analysis, and code‑generation pipelines, reducing hallucinations and improving deterministic behavior.
  • •Shipped multimodal STT → LLM → TTS pipelines using Gemini Live API, OpenAI Realtime, and ElevenLabs to support natural, low‑latency property‑tour conversations.
  • •Developed a chat client supporting serverless tool calling, coding agents, hybrid search, and grounding LLM outputs in property, zoning, and neighborhood datasets.
  • •Built Urai Portal and Urai Chat, enabling hybrid local/cloud LLM execution, retrieval‑augmented reasoning, code‑execution agents, and multimodal interaction for real‑estate workflows.
  • •Modernized legacy React forms into AEM/EDS‑compatible architectures to support faster digital experiences and future platform expansion.
    • OPTUM, Full Stack Engineer
    11/2022 – 12/2024 | Miami, FL
  • •Engineered large‑scale Java microservices and React/Next.js applications supporting clinical workflows, regulated environments, and high‑volume patient‑facing experiences.
  • •Built personalized intake flows and smarter conversion paths using structured data, semantic retrieval, and AI‑driven extraction pipelines grounded in clinical guidelines and historical notes.
  • •Developed LLM‑powered diagnostic and helper tooling connected to internal APIs and MCP‑style interfaces, enabling unified investigation across patient events, payments, CRM records, orders, provider data, alerts, and distributed system failures.
  • •Shifted AI agents from basic code‑generation to operational diagnosis, enabling LLMs to trace production issues, analyze user‑impacting failures, and propose actionable remediation steps.
  • •Implemented pgvector‑based semantic search to unify retrieval across clinical documentation, operational logs, secure messaging systems, and PHI‑safe telemetry.
  • •Added AI observability including drift detection, latency monitoring, and structured model‑behavior logging to improve reliability and accelerate root‑cause analysis.
  • •Integrated Gemini Enterprise models into clinician‑facing assistants for guideline retrieval, patient‑event analysis, and operational diagnostics, reducing hallucinations and improving accuracy.
  • •Built Vertex AI Workbench notebooks and automated MLOps workflows using Vertex Pipelines, Cloud Functions, Pub/Sub, and Vertex AI Vector Search for unified semantic retrieval across PHI‑safe datasets.
  • •Architected HIPAA‑aligned Vertex AI deployments with encryption, RBAC, VPC‑SC, audit logging, and compliant data flows to ensure safe operation in regulated healthcare environments.
  • •Implemented Terraform scripts and DevOps automation to standardize deployments, reduce operational overhead, and strengthen environment consistency across distributed systems.
  • •Performed deep troubleshooting, unit testing, and production validation across both new and legacy applications, ensuring reliability in mission‑critical clinical workflows.
  • •Partnered with clinicians, compliance teams, and product engineering to validate AI‑assisted workflows, productize successful prototypes, and improve onboarding speed while reducing manual configuration.
    • MarqVision, Backend Developer
    05/2020 – 09/2022 | Los Angeles, CA
  • •Built high-performance storefronts and merchant dashboards using React, Next.js, TypeScript, Redux, Zustand, React Query, and server-side rendering to support thousands of concurrent shoppers.
  • •Designed and implemented backend microservices in Node.js, Elixir/Phoenix, Absinthe, and Python (FastAPI) for catalog management, pricing, promotions, checkout, and order orchestration, improving order-processing reliability and reducing latency for customer transactions.
  • •Architected real-time inventory and pricing systems using PostgreSQL, Redis, DynamoDB, Kafka, and event-driven microservices to ensure consistency across storefronts, warehouses, and third-party logistics providers.
  • •Developed GraphQL and REST APIs powering merchant tools, storefront rendering, and partner integrations, improving API performance and reducing latency across global regions.
  • •Built serverless functions on AWS Lambda for high-volume workloads including cart updates, tax calculations, and fraud-detection triggers, enabling near-real-time transaction processing and lowering compute costs.
  • •Improved checkout performance by 35% through caching strategies, query optimization, and distributed session management.
  • •Created internal SDKs in TypeScript and Python for partner developers to integrate with BigCommerce’s commerce engine, reducing integration time and improving developer adoption.
  • •Enhanced platform reliability with OpenTelemetry tracing, structured logging, and automated CI/CD pipelines using GitHub Actions and CircleCI.
  • •Collaborated with product, design, and SRE teams to launch three new merchant features and improve storefront load time, maintaining 99.9% uptime during peak retail events.
    • Google, Frontend Developer
    04/2018 – 04/2020 | Lubbock, TX
  • •Built high-performance, responsive web applications using React, Next.js, TypeScript, and modern JavaScript, improving load times, interactivity, and overall user experience across multiple Google product surfaces.
  • •Collaborated with UX/UI designers, product managers, and accessibility teams using Figma and Google’s internal design system to deliver intuitive interfaces that met performance and accessibility standards, resulting in higher user satisfaction.
  • •Optimized frontend performance through code splitting, lazy loading, tree-shaking, bundle analysis, and build-level optimizations, reducing initial load times and improving Lighthouse scores.
  • •Integrated frontend applications with backend services using REST APIs, GraphQL, and WebSockets, enabling seamless real-time data flow and interactive user experiences.
  • •Implemented component-driven architecture using reusable UI patterns, Storybook, and internal Google tooling to improve consistency and velocity.
  • •Strengthened application reliability through unit testing, integration testing, and end-to-end testing using Jest, Cypress, and internal Google test frameworks.
  • •Improved developer workflows by contributing to internal libraries, shared UI components, and build tooling, reducing onboarding time and improving cross-team collaboration.
    • Google, Software Engineering Intern
    01/2018 – 03/2018 | Lubbock, TX
  • •Supported development of internal full-stack tools with Python (FastAPI), Java, and JavaScript, adding new API endpoints and UI features that enabled engineers to automate routine tasks.
  • •Assisted in building backend components on GCP using Cloud Functions and Pub/Sub, creating data-processing workflows and internal APIs that reduced manual data handling for operations teams.
  • •Developed React and TypeScript UI components for internal dashboards, fixing layout bugs and improving usability, which shortened the time users spent navigating the dashboards.
  • •Performed testing and debugging with pytest and conducted code reviews in GitHub, catching defects early and maintaining compliance with Google engineering standards.
  • •Learned scalable system design and distributed architecture principles through mentorship, applying them to design a real-time WebSocket service prototype that demonstrated feasibility for future projects.
  • •Improved developer workflows by updating internal documentation, refining build scripts, and contributing to shared libraries used across multiple teams.
    • Education
    Bachelor's, Computer Science, Texas Tech University
    01/2013 – 12/2017 | Lubbock, TX
    Certificates
    Google Advanced Data Analytics Certificate
    Google AI / Generative AI Skill Badges
    IBM Generative AI Engineering Certificate