Senior AI Engineer (Agentic Systems / LLMs)

Inactive
Source API remote eligibility restrictions: Argentina, Flexible within LATAMFull-TimeSenior
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Salary not disclosed
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Job Details

Languages
English (B2/C1)
Experience
5+ years of experience in professional software development; 2+ years of hands-on experience building and deploying AI / Generative AI solutions
Required Skills
PythonCI/CDPrompt EngineeringLangChain

Requirements

  • 5+ years of experience in professional software development.
  • 2+ years of hands-on experience building and deploying AI / Generative AI solutions in production.
  • Strong proficiency in Python.
  • Solid experience working with LLMs and agentic frameworks (OpenAI SDK, LangChain, LlamaIndex, CrewAI, or similar).
  • Proven experience with agentic systems, including memory/state management and multi-agent workflows.
  • Experience working with vector databases and RAG-based architectures.
  • Strong understanding of software engineering fundamentals: Git, testing, CI/CD pipelines.
  • Ability to translate business requirements into scalable, maintainable technical solutions.
  • Strong communication skills in English within a fully remote environment.

Responsibilities

  • Design, build, test, and deploy autonomous AI agents using Python and modern agentic frameworks.
  • Develop LLM-based systems that go beyond simple Q&A, enabling reasoning, planning, and execution across multi-step workflows.
  • Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases to ensure accurate, grounded responses.
  • Integrate AI agents with internal services, APIs, and production systems in collaboration with engineering and product teams.
  • Build evaluation, monitoring, and optimization pipelines for LLM-powered systems, focusing on accuracy, latency, reliability, and cost.
  • Apply advanced prompt engineering techniques and tool/function calling to enhance agent capabilities.
  • Stay current with the latest advancements in Generative AI, LLMs, and agentic architectures, applying best practices to production systems.
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