Software Engineer (AI)

TL;DR

Software Engineer (AI): Building scalable microservices, platforms, and tooling for AI-powered products with an accent on inference pipelines, embeddings, APIs, data flows, and observability. Focus on designing low-latency systems, optimizing performance and reliability, and troubleshooting production workloads.

Location: Sao Paulo, Brazil

Company

Industry leader building global connectivity infrastructure and AI platforms for developers.

What you will do

• Build, test, and deploy scalable microservices for AI systems including text and voice processing
• Design developer-friendly APIs, agents, and internal tools for AI integration across products
• Contribute to AI inference, embeddings, and platform services for real-time workloads
• Optimize performance, reliability, and observability in production
• Collaborate with Product, Infrastructure, and ML teams to deliver features
• Troubleshoot, debug, and support critical production systems

Requirements

• 3+ years of professional software development experience
• Strong understanding of async programming, concurrency, and parallel execution
• Experience building and operating microservice-based systems
• Solid understanding of distributed systems and production reliability
• Comfort working with databases at scale (relational, graph, or document)
• Strong experience building backend or platform systems supporting AI/ML workloads
• Excellent communication skills

Nice to have

• Familiarity with LLM frameworks (PyTorch, Transformers, DeepSpeed, vLLM)
• Experience with distributed computing (Ray)
• Cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
• LLM optimization (quantization, speculative decoding)
• Retrieval Augmented Generation (RAG) systems
• GPU stacks (AMD ROCm)
• Graph databases

Culture & Benefits

• Fast-paced environment focused on reliability, performance, and innovation
• Autonomy to own projects and shape product roadmap
• Collaborate with talented teams emphasizing impact and quality
• Continuous learning and growth opportunities
• Financially stable and profitable company investing in technology

Hiring process

• Shared AI engineering pipeline assessing core skills, system design, and problem-solving
• Interviews followed by squad alignment based on strengths, interests, and team needs