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Information Technology 🏢 Full Time ⭐️ Verified

Senior Agentic AI Architect

Nexus 2026 Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Are you ready to architect the future of autonomous intelligence? Nexus 2026 Labs is seeking a visionary Senior Agentic AI Architect to lead the development of next-generation AI systems. We are defining the standards for Agentic AI—intelligent agents capable of autonomous reasoning, planning, and execution in complex, real-world environments.


In this role, you will bridge the gap between cutting-edge generative models and robust, scalable software architecture. You will build the brain of our autonomous systems, ensuring they are safe, efficient, and capable of handling mission-critical tasks.


What You Will Do:

  • Design and implement scalable multi-agent architectures using LangChain, AutoGen, and custom frameworks.
  • Develop advanced memory and state management systems to enable long-term context retention and learning.
  • Optimize inference pipelines for high-throughput, low-latency environments.
  • Collaborate with product and research teams to integrate agents into production workflows.
  • Research and prototype novel techniques in Reinforcement Learning for autonomous decision-making.

Responsibilities

  • Architect robust multi-agent systems capable of complex task decomposition and execution.
  • Implement memory architectures (RAG, Vector DBs) to support agent continuity.
  • Write clean, maintainable Python code and contribute to open-source frameworks.
  • Conduct code reviews and mentor junior engineers on best practices in AI engineering.
  • Ensure system scalability and reliability under high load.

Qualifications

  • PhD or Master’s in Computer Science, AI, or a related field (or equivalent experience).
  • 5+ years of professional experience in Machine Learning, NLP, or Deep Learning.
  • Strong proficiency in Python and C++.
  • Deep understanding of Large Language Models (LLMs) and their limitations.
  • Experience with vector databases (Pinecone, Milvus) and RAG architectures.
  • Experience with containerization (Docker, Kubernetes) and cloud platforms (AWS/GCP).

Required Skills

Python Machine Learning NLP LLMs LangChain PyTorch Vector Databases RAG System Design Kubernetes AWS

Ready to Take This Challenge?

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