Tech Remote · 2 days ago

AI Engineer/CTO Co-founder

California, United States

Full-time

Remote

Senior Level, Lead/Staff

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IT Services and IT Consulting

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Responsibilities

Design and build autonomous multi-agent systems that manage complex workflows and dynamically adapt, implementing function-calling mechanisms to trigger specific actions based on user interactions (human-in-the-loop).

Manage the control flow for multi-agent systems, integrating AI models with knowledge graph systems and Retrieval-Augmented Generation (RAG) for real-time, interactive support solutions.

Integrate AI agents into existing workflows, ensuring a high-quality user experience. Stay updated with advancements in AI to apply new techniques and troubleshoot deployed solutions effectively.

Enhance model accuracy and responsiveness in natural language understanding and generation. Conduct testing and evaluation with tools like LangSmith, iterating based on performance metrics.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

LLM-based applicationPythonLangChainAutoGenLlamaIndexLangGraphLLM evaluation metricsLangSmithDevOpsCI/CD pipelinesTerraformDockerAWSGCPAzureAI-driven Technology experience

Required

Demonstrated ability building an LLM-based application using models such as GPT, Claude, or Anthropic.

Proficiency in writing Python for API-enabled scripts.

Experience with LangChain, AutoGen, or LlamaIndex and orchestration frameworks like LangGraph.

Familiarity with LLM evaluation metrics and tools like LangSmith.

Extremely collaborative and interactive personality with the enthusiasm to help build a well-functioning and high-performance business.

Previously worked at an AI driven Technology company that builds AI/LLM products and is not a non technology company that is only using AI.

Preferred

DevOps Experience: Skilled in CI/CD pipelines, Terraform for Infrastructure as Code, and Docker for containerization to streamline deployment and enhance scalability.

Cloud Technology Stack: Proficient with AWS, GCP, or Azure, including building and deploying AI/LLM models using cloud-native tools for optimized performance.