Looking for a job

Chris Francis

@chrisfrancis

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Senior software engineer building voice AI and clinical platforms with strong backend and full-stack execution.

India

Message

What I'm looking for

I’m looking for a remote role where I can build and scale voice AI and clinical platforms, own backend/full-stack services and APIs, and lead teams through mentoring, strong engineering practices, and measurable reliability improvements.

I’m a Senior Software Engineer who builds reliable systems around voice AI and clinical workflows—turning complex call, transcription, and evaluation needs into scalable production services. At MedWatchers, I built and now maintain the Outreach REST API (FastAPI, PostgreSQL) that processes around 800K requests a day on EKS and I own it through implementation, testing, and production monitoring.

I design and run voice AI agents for outbound calls to pharmacies, processing about 100K calls per month with over 80 percent resolved without human involvement. I also built an LLM-based pipeline (Whisper, Claude Sonnet on AWS Bedrock) that analyzes around 1000 calls per day, reducing QA staffing and saving around $180K annually.

Outside of work, I also build side projects, ranging from CLI tools (https://frank-chris.github.io/burrow) to party games (https://zero-in-now.vercel.app, made with NextJS and Supabase).

Experience

Work history, roles, and key accomplishments

Current

Senior Software Engineer

Current

MedWatchers, Inc.

May 2024 - Present (2 years 3 months)

Built voice AI agents for outbound pharmacy calls, including transcription and LLM-based evaluation to reduce QA staffing and human escalation. Designed and implemented data pipelines and internal APIs/apps using FastAPI, PostgreSQL, React, and AWS services while leading a small team.

Retell AI Voice AI Whisper LLM Evaluation Call Transcriptions Python Pandas S3 ETL fastAPI PostgreSQL NICE CXOne Pytest React TypeScript Alerting GitHub Actions CI CD

Research Intern

Caltech

May 2021 - Jul 2021 (2 months)

Developed a Python framework to pre-process and visualize low-biomass metagenomic samples, and built LSTM/CNN models in TensorFlow to classify bacterial species. Achieved 81% accuracy on bacterial classification.

Python Metagenomics Data Visualization TensorFlow CNN Machine Learning