Senior AI Engineer - Agent Creation and Orchestration
New
J
JobgetherAI software
Based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- Node.jsTypeScriptNest.jsCI/CDMicroservices
Requirements
- Bring strong professional experience in software engineering and the technical maturity to build and maintain production-grade applications.
- Demonstrate hands-on proficiency in Node.js, TypeScript, and NestJS.
- Know modern software architecture practices, including microservices, event-driven architectures, messaging systems, CI/CD, TDD, and automated testing.
- Have experience using AI-assisted software development tools and integrating AI into the software development lifecycle.
- Understand agent creation and orchestration, including agents, subagents, reusable skills, commands, and execution harnesses.
- Understand foundation models, API-based model integration, RAG, tool orchestration, multi-agent architectures, and agentic application frameworks and runtime environments.
- Have experience evaluating AI systems through behavioral testing, reliability assessments, red teaming, and mitigation of hallucinations and prompt injection.
- Be familiar with vector databases, contextual data pipelines, and integrating internal data sources for retrieval-based AI applications.
- Understand token economics, inference-cost optimization, context management, and quality, performance, and cost trade-offs.
- Know application observability, monitoring, debugging, security, privacy, and production-readiness practices.
- Be able to assess AI-generated recommendations and select effective solutions for complex engineering problems.
- Maintain expertise in a core technology stack and validate specialized work produced by AI agents.
Responsibilities
- Architect, develop, test, and deliver end-to-end software solutions using an agentic Software Development Lifecycle.
- Build and orchestrate AI agents, subagents, reusable skills, commands, and harnesses to automate development activities.
- Establish reusable agent capabilities and share them across engineering teams.
- Apply software architecture practices including microservices, event-driven systems, messaging queues, CI/CD, TDD, and automated testing.
- Review AI-generated code and outputs against technical specifications, quality standards, security requirements, and business objectives.
- Design evaluations, automated tests, and red-teaming strategies for AI-powered solutions and agents.
- Optimize token usage and inference costs through context management, RAG strategies, agent interaction patterns, and model-call optimization.
- Implement agentic architectures, context-data pipelines, observability, and application-level guardrails.
- Coordinate with AI Deployment Engineers on deployment, operational readiness, and production requirements.
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