Arquiteto de Software​

Accountabilities:

• Translate architectural solutions into detailed implementation designs, producing Low Level Designs and providing technical guidance to software developers on complex design and implementation decisions.
• Act as a technical reference and knowledge multiplier for the team, promoting standardized development practices, reusable platformization patterns, and architectural frameworks that improve engineering productivity.
• Define the technical architecture for AI agents, including agent harness patterns, orchestration strategies, tool integrations, memory and context mechanisms, and multi-agent architectures.
• Ensure that applications meet agreed non-functional requirements related to security, compliance, resilience, performance, efficiency, and other architectural governance standards.
• Guide engineering teams in evaluating business objectives, architectural trade-offs, quality attributes, technical risks, and long-term implications before making software design decisions.
• Design and detail integration flows and asynchronous messaging architectures, ensuring secure and reliable data movement across high-volume environments.
• Establish and maintain reference architecture documentation, ensuring that technical decisions, standards, and reusable patterns remain accessible and up to date.
• Manage the technology lifecycle of solutions by identifying and preventing the adoption of obsolete technologies and supporting the management of architectural debt.
• Work closely with Solution Architecture and participate in architecture forums, contributing technical expertise to broader architectural decisions and governance processes.

Requirements

• Solid professional background as a Senior Software Engineer or Software Architect, with demonstrated experience designing and operating scalable, cloud-native systems.
• Advanced development skills in Python, including experience with modern frameworks and contemporary software engineering practices.
• Hands-on experience designing and operating solutions on Google Cloud Platform (GCP), including GKE (Google Kubernetes Engine), Cloud Run, Cloud Functions, and Secret Manager.
• Practical experience architecting Generative AI solutions involving LLMs, Retrieval-Augmented Generation (RAG), and agent frameworks such as LangChain, LangGraph, CrewAI, or equivalent technologies.
• Strong knowledge of AI agent architecture, including agent harness patterns, orchestration, tool integration, memory and context management, and multi-agent approaches.
• Deep understanding of Event-Driven Architecture (EDA), with practical experience using asynchronous messaging platforms and buses, preferably Apache Kafka.
• Experience designing, structuring, and consuming RESTful APIs, together with knowledge of secure data transport and payload protection patterns.
• Familiarity with technology obsolescence governance and processes for identifying, registering, and managing architectural debt.
• Strong understanding of software architecture principles, including scalability, maintainability, resilience, security, and non-functional requirements.
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
• Experience with Vertex AI, including Agent Builder and Model Registry, as well as GenAIOps or LLMOps practices such as prompt and model versioning, monitoring, and cost and token tracking, is a strong advantage.
• Knowledge of data security practices for microservices, including mutual TLS and secure credential management through secrets vaults, is considered a plus.
• Experience with agent evaluation frameworks and LLM observability tools such as LangSmith, Langfuse, or equivalent platforms is desirable.
• GCP Professional Cloud Architect or Professional Cloud Developer certification is considered an additional advantage.

Benefits

• Opportunity to work on a modern technology platform combining data, automation, cloud-native architecture, and Generative AI.
• High technical ownership and influence over architecture standards, development patterns, and technology decisions.
• Exposure to advanced AI agent architectures, LLMs, RAG, orchestration, and emerging GenAI engineering practices.
• Opportunity to contribute to scalable, resilient, secure, and high-volume technology solutions.
• Collaboration with software engineering and solution architecture teams in a technically challenging environment.
• Opportunity to act as a technical reference and contribute to the evolution of engineering practices and architectural maturity.