Claire Lindstrom
AI Software Engineer | Real-Time Voice AI | Applied LLM Systems
Frederick, MD • U.S. Citizen • Clearance Eligible
I build production AI systems.
LLM systems, agents, RAG, evaluation, real-time voice, and cloud deployment.
About
Production AI systems engineering
I build and deploy production AI systems that operate reliably in real-world environments. My work spans LLM orchestration, agentic systems, RAG infrastructure, evaluation frameworks, and real-time voice AI platforms.
I specialize in the full stack of AI infrastructure: LLM systems and agents, semantic retrieval and RAG, evaluation and testing, and real-time communication systems. My systems are designed for production—not prototypes. I focus on reliability, observability, and performance at scale.
My approach treats AI systems as distributed systems: modular orchestration layers, rigorous evaluation of LLM failure modes, and observability-first design. I build systems with proper testing, monitoring, and deployment practices.
Based in Frederick, MD. B.S. Computer Science (GPA: 3.49), University of Maryland Global Campus, 2025. Certificate: Artificial Intelligence Foundations. U.S. Citizen, eligible for security clearance.
Projects
Production AI systems I've built
Voice AI for Field Ops
Production System
Real-time voice orchestration system for field operations. Built production-grade infrastructure with reliability and latency optimizations, AWS deployment, and integrations with Twilio and OpenAI Realtime API.
What I Owned
- •Architected real-time audio streaming pipeline
- •Implemented session lifecycle and state management
- •Optimized latency and reliability metrics
- •Built monitoring and observability systems
- •Deployed and maintained AWS infrastructure
Manifest Alchemy AI
Founder Project
Agentic planning platform with personalized AI memory, structured reasoning, and RAG-powered retrieval. Built with Next.js, Supabase, and vector search for intelligent task orchestration.
RAG Evaluation & Retrieval Toolkit
Personal Build
Comprehensive evaluation framework for RAG systems measuring retrieval quality (precision/recall), latency, groundedness, and response accuracy. Includes dataset harness, regression tests, and deployment tooling.
Skills
Technologies and capabilities
Languages
- Python
- JavaScript
- SQL
- Java
- C++
AI & ML
- OpenAI APIs (Realtime & Chat)
- LLMs
- NLP
- Prompt Engineering
- AI Evaluation
- TensorFlow
Web & Backend
- FastAPI
- React
- Next.js
- HTML
- CSS
- Tailwind
- WebSockets
Databases
- PostgreSQL
- Supabase
- SQLite
- Airtable
- Pinecone
- Vector Databases
Cloud & DevOps
- AWS EC2
- Load Balancing
- Git/GitHub
- Docker
Experience
Professional background
AI Software Engineer (Voice AI Systems)
Architected and deployed production-grade Voice AI system using Twilio Media Streams, OpenAI Realtime, FastAPI, and AWS EC2. Built low-latency WebSocket audio pipelines for real-time transcription and response generation. Implemented role-based conversational logic and dynamic data retrieval from operational databases. Designed caching and pre-generation strategies to eliminate cold-start latency and call drop-offs. Debugged and resolved real-time call reliability issues in a live production environment.
Founder & AI Engineer
Designed and built full-stack AI platform focused on long-term memory and personalized reasoning. Implemented LLM-powered conversational agents with structured prompts and semantic retrieval. Built modern web interfaces using React, Next.js, and Tailwind CSS. Designed scalable data models using PostgreSQL/Supabase with vector storage.
AI & Front-End Software Engineer
Developed and maintained JavaScript, HTML, and CSS front-end components. Wrote and optimized SQL queries supporting customer and operational workflows. Assisted in training and evaluating AI chatbot systems, improving accuracy and reliability. Performed AI quality assurance, identifying hallucinations and failure cases prior to deployment.
Data & AI Solutions Specialist
Developed AI-driven VR/AR training programs using Unity, Unreal Engine, and Python. Built data-rich web platforms using React, Python, and SQL for analytics and LMS integration. Created AI-powered learning materials using ChatGPT API and Pinecone vector databases for semantic retrieval. Automated production workflows with Python, increasing efficiency by 40%. Contributed to AI-assisted visualization tools for training and performance tracking.