Senior AI Systems Architect
New
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced English proficiency
- Required Skills
- PythonSoftware ArchitectureMicroservicesPrompt EngineeringDistributed Systems
Requirements
- Strong background in Software Architecture, Platform Engineering, or Senior Software Engineering roles with enterprise-scale systems experience.
- Proven experience designing distributed systems, APIs, and microservices-based architectures.
- Hands-on experience with Agentic AI workflows, AI engineering, or applying LLMs within the software development lifecycle.
- Strong expertise in prompt engineering, context management, and optimization of LLM interactions.
- Ability to translate business logic and technical knowledge into structured workflows and agent-based heuristics.
- Solid understanding of observability, monitoring, and system behavior analysis in production environments.
- Experience with scalable system design and engineering best practices, including reliability and performance considerations.
- Strong analytical thinking, problem-solving ability, and strategic architectural vision.
- Advanced English proficiency for technical communication and documentation.
Responsibilities
- Design and orchestrate end-to-end AI-driven workflows across the software development lifecycle, from requirements definition to deployment, operations, and continuous improvement.
- Architect agent-based systems that enable autonomous execution of engineering tasks, promoting an AI-first approach to software delivery.
- Build and evolve specialized AI agents for development, QA, DevOps, product management, and operational support functions.
- Define strategies for context management, prompt engineering, and token optimization to ensure efficient and reliable LLM performance.
- Structure and maintain knowledge bases, architectural decision records (ADRs), and technical documentation to ensure consistency and reuse across AI systems.
- Develop evaluation frameworks (Evals) to monitor agent quality, accuracy, and behavior, driving continuous improvement and reducing hallucinations.
- Ensure compliance with internal standards, security policies, and architectural governance across all AI-driven systems.
- Act as a technical reference for the evolution of AI platforms and multi-agent ecosystems applied to software engineering.
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