SR. AI Engineer -Foco em Criação e Orquestração de Agentes (Agentic SDLC) in Brazil at Jobgether

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Sr. AI Engineer – Focus on Agent Creation and Orchestration (Agentic SDLC) based in Brazil.

This is an exciting opportunity for a senior-level engineer to shape the future of software development by combining advanced engineering practices with AI-powered agent orchestration. You will design, build, and deliver production-ready software solutions while using AI as a core driver throughout the software development lifecycle. A key focus will be creating and orchestrating autonomous agents, subagents, reusable skills, commands, and development frameworks that improve team productivity and accelerate delivery. You will also architect intelligent applications using foundation models, retrieval-augmented generation (RAG), tool orchestration, and multi-agent systems when required by client projects. The role combines hands-on technical leadership, pragmatic architecture, rigorous evaluation, and continuous optimization of AI-generated outputs. Working in a collaborative, remote environment, you will help modernize engineering practices while ensuring that solutions remain secure, observable, scalable, and ready for production.

• Architect, develop, test, and deliver end-to-end software solutions across relevant technology stacks, using an agentic Software Development Lifecycle (Agentic SDLC) and AI as primary development accelerators.
• Build, customize, and orchestrate AI agents, subagents, reusable skills, commands, and harnesses that automate development activities, improve team productivity, and reduce software delivery lead time.
• Establish reusable agent capabilities and share them across engineering teams to promote continuous improvement and modernize software development practices.
• Apply modern software architecture and engineering principles, including microservices, event-driven systems, messaging queues, continuous integration and delivery (CI/CD), test-driven development (TDD), and automated testing.
• Translate functional and business requirements into practical, scalable technical solutions, making deliberate architectural decisions and avoiding unnecessary complexity or over-engineering.
• Critically review AI-generated code and outputs to ensure they meet technical specifications, quality standards, security requirements, and business objectives.
• Design and implement evaluations (evals), automated tests, and red-teaming strategies to assess application behavior and validate the reliability of AI-powered solutions and the agents used to build them.
• Identify and mitigate AI-specific risks, including hallucinations, prompt injection, data leakage, and other vulnerabilities that may affect application reliability or security.
• Optimize token usage and inference costs by managing context-window size, RAG strategies, agent interaction patterns, and the number of model calls without compromising solution quality.
• When AI capabilities are part of the solution, design and implement agentic architectures using foundation models accessed through APIs, retrieval-augmented generation, tool calling, orchestration frameworks, runtime environments, system prompts, and application-level guardrails.
• Engineer context-data pipelines that integrate internal information sources and vector databases, ensuring AI agents receive reliable, relevant, and high-quality information.
• Implement observability across applications and AI agents, monitoring system behavior, identifying anomalies, and responding to performance degradation or unexpected outputs.
• Delegate specialized development tasks to purpose-built agents while maintaining an end-to-end understanding of the software lifecycle and deep technical expertise in a primary technology stack.
• Evaluate and continuously improve the effectiveness of AI agents and development workflows through measurable testing and performance assessments.
• Deliver production-ready solutions and coordinate with AI Deployment Engineers to align deployment, operational readiness, and production requirements.
• Apply responsible AI, data privacy, security, and compliance practices to ensure that solutions meet applicable client requirements and organizational standards.

• Strong professional experience in software engineering, with the technical maturity to architect, develop, test, and maintain production-grade applications.
• Hands-on proficiency in Node.js, TypeScript, and NestJS, which are the core technologies specified for this position.
• Solid knowledge of modern software architecture and engineering practices, including microservices, event-driven architectures, messaging systems, CI/CD, TDD, automated testing, and maintainable code design.
• Experience using AI-assisted software development tools and integrating AI into the software development lifecycle, with the ability to move beyond passive assistant usage and build customized agent-driven workflows.
• Practical knowledge of agent creation and orchestration, including agents, subagents, reusable skills, commands, and execution harnesses.
• Understanding of foundation models, API-based model integration, RAG, tool orchestration, multi-agent architectures, and the frameworks and runtime environments used to deploy agentic applications.
• Experience designing and implementing evaluation strategies for AI systems, including behavioral testing, reliability assessments, red teaming, and mitigation of hallucinations and prompt injection.
• Familiarity with vector databases, contextual data pipelines, and the integration of internal data sources to support retrieval-based AI applications.
• Understanding of token economics, inference-cost optimization, context management, and the trade-offs between AI solution quality, performance, and cost.
• Knowledge of application observability, monitoring, debugging, security, privacy, and production-readiness practices.
• Strong analytical thinking and technical judgment, with the ability to challenge AI-generated recommendations and select the simplest effective solution for complex engineering problems.
• Ability to work across the software development lifecycle while maintaining deep expertise in a core technology stack and validating specialized work produced by AI agents.
• Strong communication and collaboration skills to work effectively with multidisciplinary engineering teams and deployment specialists.
• A proactive, adaptable, and continuous-learning mindset, with an interest in advancing AI-native engineering practices and improving software delivery outcomes.

• Comprehensive health and dental insurance.
• Meal and food allowances.
• Childcare assistance.
• Extended parental leave.
• Access to fitness facilities and health and wellness professionals through Wellhub (formerly Gympass) and TotalPass partnerships.
• Profit-sharing program (PLR).
• Life insurance.
• Continuous learning opportunities through a dedicated professional development platform.
• Employee discount programs.
• Free online resources focused on physical health, mental health, and overall well-being.
• Pregnancy and responsible parenting courses.
• Partnerships with online learning platforms and access to language-learning resources.
• Additional employee benefits and professional development opportunities.
• Remote work arrangements. Please note that employees residing in the Campinas Metropolitan Region are required to attend the company's offices in accordance with the applicable attendance policy.
• An inclusive work environment with dedicated health and well-being resources, inclusion specialists, and affinity groups to support employees throughout their professional journey.