Piece is a holding company that acquires vertical software/AI businesses and scales them through creator-powered distribution. We believe the next Latin American giants will emerge at the intersection of AI and creator economy — software brings technology, recurring revenue, and LTV; creators bring audience, trust, and distribution. Piece brings them together.
Our edge is a proprietary creator-focused distribution engine that uses AI to automate growth across our vertical software portfolio — social media, sales funnels, content, and creator operations. Every vertical we add makes the machine smarter, compounding proprietary data on what works in creator-led distribution for AI/Software. Our goal is to hit US$10M ARR in 2026.
We're building Piece to be a home for some of the most exceptional people in the world. Talent density is non-negotiable, the bar is unreasonably high, and we expect excellence in everything we ship. Our ambition is to put Brazil on the map as a global protagonist in growth and distribution.
The Role
We're looking for a Senior Backend Engineer to be the technical pillar of the product at the first and main company in our portfolio — someone who combines technical depth with product sense, and who will own the architecture, evolution, and operation of its entire backend and AI infrastructure.
You’ll be hands‑on building and scaling the systems that orchestrate our agents, process messages in real time, and sustain the platform’s accelerated growth. This isn’t a maintenance role — it’s a building role.
If you enjoy solving complex problems in distributed systems, have a genuine interest in applied AI, and want to build something that’s reshaping how the Brazilian legal market operates, this is the place. You’ll join as part of our founding team, with competitive compensation and meaningful equity through stock options.
What you’ll do
Backend architecture and evolution
* Design and evolve the microservices that power the platform, with a focus on scalability and maintainability
* Make technical decisions autonomously — database modelling, API design, stack choices
* Raise the team’s technical bar through code review and mentorship
AI agents and LLMs
* Build and evolve our agent pipelines: orchestration, tool calling, dynamic workflows, and intelligent routing
* Optimize cost and latency of LLM calls without compromising quality
* Evolve our RAG, document processing, and voice synthesis features
Integrations and operations
* Ensure reliability of asynchronous message flows and external integrations
* Build new integrations as the product evolves
* Maintain operational stability in a multi‑tenant system with paying customers
Quality and observability
* Ensure end‑to‑end traceability across synchronous and asynchronous flows
* Structure operational metrics and maintain visibility over agent behaviour in production
* Identify and resolve performance bottlenecks before they become incidents
What we expect from you
* Proficiency with Node.js + TypeScript in high‑throughput production systems — including API design, modular architecture, and long‑term maintainability decisions
* Strong grasp of event‑driven and asynchronous architectures: message queues, pub/sub patterns, idempotency, retry logic, and failure isolation in distributed systems
* Experience designing multi‑tenant SaaS backends: data isolation strategies, per‑tenant configuration, and operational complexity at scale
* Solid database modelling — relational (schema design, indexing, query optimisation) and NoSQL (document modelling, consistency trade‑offs) — and ability to choose the right store for the problem
* Hands‑on cloud experience (GCP preferred): Cloud Run, Pub/Sub, Firestore/Cloud SQL, IAM, and observability tooling
AI & Agent Systems
* Experience building LLM‑powered pipelines in production — not just calling APIs, but thinking through orchestration, context management, fallback behaviour, and output reliability
* Understanding of agent architectures: tool calling, multi‑step reasoning, routing logic, state management across turns, and how to make agents predictable in real‑world conditions
* Practical knowledge of RAG pipelines: chunking strategies, embedding selection, retrieval quality, and reranking — and awareness of where RAG breaks down
* Cost and latency awareness in LLM usage: prompt engineering for efficiency, model selection trade‑offs, caching strategies, and when not to use an LLM
* Familiarity with evaluation and observability for AI systems: how to measure agent quality, detect regressions, and maintain visibility over non‑deterministic behaviour in production
Product & Technical Judgment
* Ability to reason about product trade‑offs, not just technical ones — understands what to build, what to cut, and why
* Comfortable operating with ambiguity: capable of going from a fuzzy problem to a concrete technical proposal without needing a fully‑specified ticket
* Has opinions about prioritisation and isn’t just an executor — questions requirements upstream and thinks about user and business impact before writing the first line of code
* Uses AI tools natively in the development workflow (coding assistants, automated testing, code review, documentation) and stays current with the evolving tooling landscape. Understands that development velocity is a competitive advantage, not just an output metric
Quality & Operational Maturity
* Ability to instrument distributed systems end‑to‑end: structured logging, distributed tracing, alerting, and SLO thinking
* Experience operating asynchronous flows with real customers: understanding of what breaks silently and how to catch it before they do
* Comfort with ambiguity — able to define the right technical approach when the problem itself is still being figured out
Nice to have
* Python for ML/data pipelines or LLM experimentation
* Go for latency‑critical or high‑throughput services
* Experience with LLM fine‑tuning, RLHF, or systematic model evaluation
* Background in regulated or compliance‑heavy domains (legaltech, fintech)
What we offer
* Founding team seat — real ownership of what gets built and how
* Competitive compensation
* Meaningful stock options
* On‑site environment with high talent density and direct exposure to founders
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