Senior AI Engineer
New
J
JobgetherAI engineering
Based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Fluent English proficiency (B2 level or higher), both written and spoken.
- Required Skills
- PythonReactCI/CDPrompt Engineering
Requirements
- Have proven experience as a Senior AI Engineer building and deploying AI-powered applications in production.
- Have strong Python backend development skills, including APIs, services, and data processing.
- Have solid frontend development experience with React.
- Have hands-on experience with LLM-powered applications, including prompt and context engineering, RAG, tool orchestration, and multi-agent architectures.
- Have experience with vector databases and context data pipelines.
- Know AI evaluation methodologies, automated testing, probabilistic behavior assessment, and AI red-teaming practices.
- Have practical experience using AI coding agents and developing custom agents, skills, or commands.
- Understand software architecture and engineering fundamentals, including microservices, event-driven architectures, CI/CD, TDD, and observability.
- Have experience with code analysis, document processing, parsing, or text extraction solutions.
- Be able to provide technical leadership, conduct code reviews, solve complex engineering challenges, and mentor team members.
- Have fluent English proficiency at B2 level or higher, both written and spoken.
Responsibilities
- Lead AI-powered solutions from architecture and technical design through implementation, testing, and production deployment.
- Design agentic AI architectures with LLM integrations, RAG, tool orchestration, runtime frameworks, system prompts, and safety guardrails.
- Develop context data pipelines using internal sources and vector databases.
- Build backend services and APIs with Python and user-facing applications with React.
- Create and share reusable AI development assets, including agents, skills, subagents, commands, and development harnesses.
- Evaluate AI solutions through automated testing, evaluation frameworks, and red-teaming to identify hallucinations, prompt injection vulnerabilities, and potential data leaks.
- Optimize token consumption and inference costs through context management, RAG strategies, and agent-call orchestration.
- Implement observability across applications and agent workflows to monitor performance, behavior, output quality, and operational costs.
- Lead technical discussions, review code, mentor engineers, and collaborate with AI Deployment Engineers and AI Orchestrators.
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