Design and scale async REST/WebSocket APIs in Python (FastAPI or similar), using dependency injection, type hints, and clean, vertical-slice architecture
Implement multi-agent workflows (sequential, concurrent, and handoff patterns) to route work among specialized LLM agents
Integrate multiple LLM providers behind a provider-agnostic layer, enabling A/B testing and cost-aware routing rather than hard-coding a single vendor
Build and maintain Retrieval-Augmented Generation (RAG) pipelines against vector stores such as pgvector, Chroma, or a managed vector search service
Design schemas and evolve data models (SQLAlchemy/SQLModel-style ORM plus migrations) that support both traditional application logic and AI-driven features
Instrument services with structured logs, distributed tracing, and cost/latency metrics, holding a real bar for p95 response time under production load
Maintain end-to-end CI/CD ownership, lint, type-check, test, package, and deploy, so releases are boring and safe
Champion AI across the delivery lifecycle, not just code generation, and set the guardrails the team works inside: how generated code gets reviewed, how provenance and licensing are checked, and what customer data may enter a prompt
Move ideas from concept to production fast, with real ownership over architecture and outcomes, not just tickets
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Benefits
Work-life balance.Meaningful work, balanced by a culture that respects your time
We care about you.We offer aMeal Voucher or Food VoucherworthR$44.55 per working day. Comprehensivehealth and dental insurance, with no employee co-pay
Well-being programincludingGympass, online meditation, and telemedicine
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Educational platform: pursue undergraduate and graduate degrees, language courses, and much more through100+ educational institutionsat highly affordable rates
Life insurancecoverage
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Requirements
5+ years building production backend systems in Python, with real async I/O experience
Hands-on experience designing and scaling REST or async APIs, including dependency injection and clean service architecture
Practical experience integrating LLM APIs (OpenAI, Gemini, or similar) and building AI-readiness into backend systems from the ground up
Real experience with vector databases and RAG-style retrieval architectures, not just reading about them
AI-augmented delivery: you use AI across the delivery lifecycle, not just code generation, and can say which models suit which problems and where you don’t use them. You review AI-generated code knowing its failure modes, and you have set the guardrails others work inside – review expectations for generated code, provenance and licensing checks, and clear boundaries for what customer data enters a prompt and what a security or compliance review will ask for
End-to-end CI/CD ownership, from build and test through packaging and deployment
A self-starting, go-getter mindset: comfortable with ambiguity, fast iteration, and owning a problem until it’s actually solved
Bonus: experience with multi-agent orchestration frameworks (Semantic Kernel, LangChain, or similar) and LLM cost/observability tooling