Responsibilities

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Integrate Generative AI models, including LLMs, with external APIs, tools, and databases using secure and efficient orchestration patterns.

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Design, develop, and deploy AI workflows and Agentic AI solutions, enabling the seamless orchestration of intelligent agents to plan and perform tasks while leveraging autonomous and/or human-in-the-loop paradigms.

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Implement and optimize multi-agent systems, leveraging standards and protocols such as Model Context Protocol (MCP) and emerging frameworks for agent interoperability.

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Develop evaluation frameworks, metrics, and checkpoints for agent autonomy, performance, and safety, ensuring compliance with moderation, security, and ethical standards.

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Ensure robust AI agent operations by applying observability, monitoring, and MLOps best practices, facilitating reliable deployment pipelines and continuous performance optimization.

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Collaborate closely with data experts, orchestrating AI model selection, tuning, and performance validation to meet specific agent-based application needs.

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Communicate complex AI concepts, systems, and decisions effectively to technical and non-technical stakeholders, promoting transparency and trust in AI delivery

Requirements

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Proven experience designing and deploying AI architectures, with expertise in Generative AI, NLP, LLM integration, and software engineering.

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Strong background in building software platforms (Python/Django, Java/Spring, TypeScript/Express, etc.) capable of API integration and orchestration.

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Strong understanding of the trade-offs between various generative AI models and the ability to choose the right model for specific use cases.

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Hands-on experience with function-calling and tools integration into LLM models, leveraging frameworks such as Model Context Protocol (MCP).

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Expertise in data embeddings, vector databases, and chunking strategies, understanding the trade-off between different options, and leveraging it to optimize data in

📌 AI Engineer (Buenos Aires)
🏢 GM2
📍 Buenos Aires