Senior Full-Stack Engineer

About Kedara Millions of families struggle to coordinate elder care. We're building a voice-first AI-powered care coordination app for families and caregivers—assisting them with managing in-home elder care. 1.4B 65+ seniors by 2030, 95% will age at home, and 80% of caregivers are women in the families. This is a hugely underserved population and presents a global opportunity.

Our founders scaled AI/ML features in Speech and NLP at MindMeld and BabbleLabs (both acquired by Cisco) to 100M+ users. We're a small team of product, AI/ML, and senior care experts. You'll be our founding full-stack engineer.

Read more about Kedara https://kedara.care

Linkedin https://www.linkedin.com/company/kedara-care

The Role

This role requires a systems thinker who is equally comfortable optimizing a database query and implementing a highly responsive front-end component. You will own the end-to-end development of critical features, bridging our complex AI backend and user-facing applications to deliver a seamless, responsive experience for caregivers.

You'll use AI coding tools — Claude Code, Replit — as force multipliers, shipping in days what traditional teams ship in weeks. You review and stand behind everything they produce.

What You'll Own

System Design: Design and implement modular architecture that prioritizes rapid iteration while maintaining a clear path to scale for a reliable consumer experience.
Full Stack Implementation: Own features across the entire stack — from database schemas (PostgreSQL / Firestore) and API routes (FastAPI / Python) to responsive, accessible front-end components (React + Vite / TypeScript).
Real-Time Voice AI Performance: Minimize latency across all communication pathways, especially for streaming voice and text responses. Design optimistic UI patterns and local state management for Voice AI + LLM interactions.
Voice-to-Visual Bridge: Ensure caregivers can speak to the assistant and immediately review structured information on screen — bridging voice-first interactions with visual feedback.
Edge/Cloud Communication: Work with the AI team to optimize low-latency voice experience on device and the centralized LLM/inference layer.
Data Pipelines: Design resilient pipelines for logging user interactions, model inputs, and outputs — including PII scrubbing — critical for continuous improvement and future fine-tuning.
Deployment: Use Docker and GCP (Cloud Run, Firebase Hosting, Secret Manager) to deploy and scale application services reliably across our cloud infrastructure.

Requirements