Staff AI Engineer
J
JobgetherAI Technology
Based in BrazilFull-TimeStaff
Salary not disclosed
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Job Details
- Required Skills
- PythonJavaKubernetesTypeScriptCI/CD
Requirements
- Proven experience architecting and implementing AI systems with a strong software engineering foundation.
- Strong programming experience with technologies such as Python/Django, Java/Spring, TypeScript/Express, and API integrations.
- Hands-on experience with agentic AI frameworks such as LangGraph, Google ADK, OpenAI Agents SDK, or CrewAI.
- Experience with tool-calling, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and AI orchestration patterns.
- Strong knowledge of embeddings, vector databases, chunking strategies, and context optimization techniques.
- Experience with AI evaluation and observability platforms such as RAGAS, LangSmith, Arize, or Braintrust.
- Experience building scalable systems using CI/CD, containerization, Kubernetes, and cloud AI services such as AWS Bedrock or Azure AI.
- Advanced prompt engineering and context engineering skills, with the ability to communicate AI concepts clearly to different audiences.
- Experience designing reliable, secure, and high-performance AI applications.
- Strong problem-solving skills, ownership mindset, and ability to work in collaborative, distributed teams.
Responsibilities
- Own the end-to-end architecture of AI-powered systems, including Generative AI applications, agentic workflows, and multi-agent ecosystems.
- Design scalable approaches for agent coordination, context management, memory strategies, and human-in-the-loop interactions.
- Lead technical decisions around AI model selection, architecture trade-offs, latency, cost optimization, accuracy, scalability, and governance.
- Define evaluation frameworks, monitoring strategies, and safety guardrails to ensure reliable AI system performance.
- Design AI data architectures, including embedding strategies, vector databases, retrieval pipelines, and context management solutions for large-scale data.
- Ensure production readiness through CI/CD practices, containerized deployments, cloud integrations, observability, and reliability engineering.
- Provide technical leadership by mentoring engineers, guiding architecture decisions, and communicating complex concepts to technical and business stakeholders.
- Collaborate with engineering, data, product, and operations teams to align AI initiatives with strategic goals.
- Research and evaluate emerging AI models, frameworks, and architectural patterns to continuously improve AI capabilities.
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