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AI Engineer

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Onsite

AI Engineer

TIVIT LATAM

AI Engineer

Skills

DockerPythonGitMachine LearningAINo-code Automationsn8nAzure

12 days ago

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Important information


Contract type:

Permanent

Location:

United States

Work mode:

Onsite

Published on:

15 October 2026

What they need


At TIVIT Almaviva Group, we are driving the future of technology by integrating advanced artificial intelligence into core business operations. We are currently looking for a professional specialized in AI Automation to design, develop, and deploy innovative solutions leveraging Large Language Models (LLMs) and complex workflows.

Education

  • University degree in Systems Engineering, Computer Science, Mathematics, or closely related technical fields.

Minimum Requirements

  • At least 2 years of demonstrable experience in software development, with a strong focus on deploying Artificial Intelligence or Machine Learning projects in production environments.
  • Proficiency in developing and maintaining production-grade code in Python, applying strict best practices for typing, packaging, and asynchronous programming.
  • Solid application of software engineering principles, including version control (Git), code reviews, and automated testing.
  • Demonstrated capability in prompt engineering and structured output generation to interact efficiently with LLM APIs (such as Azure OpenAI and Anthropic).
  • Strong analytical and critical thinking competencies, paired with effective communication and a focus on applied innovation.
  • Speaking and Writing in English: BETWEEN B2 - C2 level IS A MUST

Desirable Requirements

  • Solid theoretical and practical understanding of LLM fundamentals and API integrations.
  • Experience designing and building Retrieval-Augmented Generation (RAG) architectures, effectively managing chunking, embeddings, vector databases, and reranking processes.
  • Background in developing multi-step workflows and autonomous AI agents (tool calling, planning, memory) utilizing frameworks such as LangChain or LangGraph.
  • Knowledge of LLMOps practices, including model evaluation, continuous monitoring, and guardrail implementation.
  • Familiarity with containerization (Docker) and workflow automation engines (such as n8n or Camunda).
  • A strong orientation toward continuous learning and adaptability in the fast-evolving artificial intelligence landscape.

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