Open to opportunities

Amro Eid

@amroeid

Message

I build production AI voice agents, fine-tuned LLM systems, and scalable MLOps platforms.

Palestine, State of

Message

What I'm looking for

I'm looking to build and deploy practical AI products, especially LLM, voice AI, and MLOps systems, where I can own production engineering challenges and continue growing across machine learning and cloud platforms.

At Neuvox, I build production AI voice agents for call centers, including a real-time FastAPI and Pipecat pipeline over Twilio WebSocket streams with 500–900ms STT-to-LLM-to-TTS latency.

At Launched, I fine-tuned Mistral/Magistral 24B for personalized outbound email generation, deployed a GPTQ-quantized vLLM endpoint on an A100 at roughly 90 tokens per second, and built RAG and MLOps workflows across Azure, Docker, Dagster, and GitHub Actions.

I've also built high-performing deep learning systems for essay scoring and leukemia classification, alongside real-time tweet analytics pipelines. I'm ranked #1 in my department while completing a Computer Science degree with an Artificial Intelligence focus.

Experience

Work history, roles, and key accomplishments

Current

AI Software Engineer

Current

Neuvox

Jan 2026 - Present (7 months)

Engineered a real-time voice AI pipeline using FastAPI and Pipecat on ECS Fargate over Twilio WebSocket streams, achieving 500-900ms end-to-end turn latency. Built a multi-provider LLM/STT/TTS abstraction layer and a multi-tenant AWS Lambda REST API with DynamoDB and Cognito auth.

fastAPI Pipecat AWS ECS Fargate Twilio OpenAI GROQ Mistral Deepgram ElevenLabs Cartesia AWS Lambda DynamoDB Cognito S3 GitHub Actions CI CD

ML R&D Engineer

Launched

Jul 2025 - May 2026 (10 months)

Fine-tuned LLMs for an AI-first outbound sales platform, deploying a GPTQ-quantized vLLM endpoint and building a RAG system. Migrated the stack to Azure and implemented MLOps lifecycle with Dagster and CI/CD.