Building the world’s first emotional intelligence layer for AI, specifically for an AI companion app named Robyn, by developing the backend infrastructure for conversations, memory, real-time personalization, voice/chat interface, scalable emotional intelligence infrastructure, and secure APIs.
Requirements
- C / .NET / ASP.NET backend api layer (have experience in this or something similar like Java/Spring and can learn quickly)
- Python microservices (FastAPI or AWS Lambda)
- Docker containers or AWS Lambda (SnapStart)
- PgVector
- AWS infrastructure: Lambda, ECS, S3, RDS (Postgres), CloudFront, IAM, Route53
- OpenSearch
- Terraform
Responsibilities
- You'll work and add to our C / .NET / ASP.NET backend api layer (have experience in this or something similar like Java/Spring and can learn quickly) and progressively add many Python microservices (FastAPI or AWS Lambda) with modular, AI-native architecture in mind to build our intelligence layer.
- Build and maintain REST and GraphQL APIs consumed by our iOS client; low-latency, resilient, and well-instrumented.
- Architect a microservice-style ML model serving backend deployed via Docker containers or AWS Lambda (SnapStart), backed by async eventing and pub/sub where needed.
- Own CI/CD, rollback strategies, logging, error handling — the backend is your domain, end-to-end.
- Architect and manage existing vector DB (PgVector) and potentially add more to power retrieval-augmented generation, evolving memory, and personalization.
- Integrate and scale inference with OpenAI, Claude, Llama, or other models. Build wrappers, manage caching, fallbacks, and prompt routing logic.
- Manage our AWS infrastructure and add to our current stack with new innovate technologies: Lambda, ECS, S3, RDS (Postgres), CloudFront, IAM, Route53 — you’ll be the one making the call on architecture and trade-offs.
Other
- You have 6 - 15 years of experience in backend or full-stack development; building 0-1 products or teams in a startup environment
- You’ve shipped entire production backends at high-growth early-stage startups — you know how to move fast and still write code you don’t hate six months later.
- You’ve integrated or scaled LLM-based products — bonus if you’ve done it with emotion, memory, or personalization layers.
- You care about systems thinking, fast response times, clean abstractions, and building infra that won’t fall over under load.
- You’re able to figure things out quickly and dive in wherever needed. There’s no “that’s not my job” here.
