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Principal Agentic AI Engineer
PresencialTiempo completoAI EngineeringEnterprise Software
Lead the technical vision, architecture, and implementation of production-grade agentic AI platforms and enterprise systems. This role combines technical leadership with hands-on development, ecosystem engagement, and global representation.
Responsabilidades
- Lead the technical vision, architecture, and roadmap for agentic AI platforms and systems.
- Design and implement agentic AI capabilities including planning, multi-agent collaboration, memory, RAG, tool use, evaluation, and observability.
- Translate enterprise requirements into scalable, production-ready AI solutions.
- Evaluate emerging agentic AI technologies and drive their integration into the platform.
- Provide hands-on technical leadership across architecture, prototyping, implementation, and optimization.
- Represent the organization globally and help grow its developer and enterprise ecosystem.
Requisitos
- Bachelor’s or master’s degree in computer science, AI, engineering, or a related field; a Ph.D. is advantageous.
- At least 8 years of software engineering and AI experience, including hands-on work with LLMs, generative AI, and agentic AI.
- Recognized expertise in agentic AI and substantial contributions to agent platforms, frameworks, or enterprise AI solutions.
- Proven experience architecting, building, and scaling production-grade agentic AI platforms and systems.
- Deep knowledge of reasoning and planning, memory, retrieval-augmented generation, tool use, context engineering, multi-agent systems, evaluation, and observability.
- Strong expertise in Python, software architecture, distributed systems, cloud platforms, APIs, and AI infrastructure.
- Ability to combine technical vision with hands-on execution from architecture and prototyping through production.
- Strong technical leadership, presentation, and communication skills.
Se valora
- Experience with enterprise AI architecture focused on reliability, security, scalability, performance, and cost efficiency.
- Experience with leading agentic AI platforms, frameworks, or ecosystems.
- Experience contributing to global open-source and developer communities.
- Evidence of industry influence through conferences, publications, research, patents, advisory work, or enterprise engagements.
- Experience with physical AI and embodied agent systems.