Founding Platform Engineer - São Paulo
há 3 dias
Descrição do trabalho
About the RoleWe're building a platform ) that lets anyone create, deploy, and manage autonomous AI agents with custom skills, tool integrations, and production-ready UIs. You'll work across backend, frontend, and infrastructure — reporting to the Product Architect.
What You'll Do
Build and maintain the core agent orchestration engine, skill execution, and state management
Develop the builder UI in for composing agents and workflows
Implement real-time communication layers (WebSockets, SSE) for live agent interactions
Own backend microservices in Python and Go
Design and optimize MongoDB schemas for multi-tenant, high-throughput workloads
Manage cloud infrastructure, deployments, and observability on AWS
Required Experience
Backend — Python & Go
Building scalable microservices in Python (FastAPI) and Go
WebSocket and SSE implementation; diagnosing socket-level issues
MongoDB at scale — sharding, indexing, replica sets
Authentication mechanisms — OAuth 2.0, JWT
Pub/Sub patterns and event-driven architecture
Queue management, state persistence, retry and dead-letter handling
Understanding of latency, networking, and distributed systems performance
Frontend —
CSR vs. SSR trade-offs and when to apply each
Diagnosing hydration issues
State management across client and server boundaries
Scalable component architecture
Deployment, reverse proxying, and build optimization
Infrastructure & Observability
ECS management and CloudWatch log aggregation
Cloud networking basics — VPCs, load balancers, security groups
Managed services — DocumentDB, Redis
SigNoz or similar observability tools
Version Control
Git workflows — branching strategies, PR reviews, conflict resolution
Repository management for microservice codebases
System Design
LLD:
Design patterns, SOLID principles, API contract design
HLD:
Distributed system architecture, service decomposition, scalability patterns, fault tolerance
Database Design:
Schema modeling (SQL & NoSQL), indexing, query optimization, consistency trade-offs
Nice to Have
Experience with AI agent frameworks — tool use, skill orchestration, multi-step reasoning
LLM integration patterns — prompt chaining, function calling, structured outputs
Firecracker MicroVMs or E2B-style sandboxed execution environments
Sandbox isolation, security boundaries, and secure code execution
Agent memory and long-running task orchestration
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