AI Agent Engineer; Machine Learning

Job Description

The Agentic Tribe is revolutionizing the chatbot and voice assistance landscape with Gen3, a cutting‑edge AI Agent system that is pushing the boundaries of conversational AI. Gen3 isn't your typical chatbot; it's a goal‑oriented, dynamic, and truly conversational system capable of reasoning, planning, and adapting to user needs in real time. By leveraging a multi‑agent architecture and advanced language models, Gen3 delivers personalized and engaging user experiences, moving beyond scripted interactions to handle complex tasks and "off‑script" inquiries with ease.

We're seeking a highly experienced and influential AI Agent Engineer to join our team. In this role, you'll be dedicated to driving innovation and technical leadership at the forefront of AI technology, with a focus on designing, developing, and deploying intelligent, autonomous agents that leverage Large Language Models (LLMs) to streamline operations. You'll shape the cognitive architecture for our AI‑powered applications, creating systems that can reason, plan, and execute complex, multi‑step tasks, and guiding other engineers.

You'll own critical, cross‑cutting technical initiatives that impact multiple teams, serve as a go‑to expert for complex problems, and proactively engage with a broad range of stakeholders to influence strategy and execution.

• Architect, design, and lead the development of robust, stateful, and scalable AI agents using Python and modern agentic frameworks (e.g., Lang Chain, Llama Index), setting technical direction and best practices for engineering teams.
• Strategize and oversee the integration of AI agent solutions with existing enterprise systems, databases, and third‑party APIs to create seamless, end‑to‑end workflows across the product, identifying and mitigating architectural risks.
• Evaluate and select appropriate foundation models and services from third‑party providers (e.g., OpenAI, Anthropic, Google), analyzing their strengths, weaknesses, and cost‑effectiveness for specific use cases.
• Own and drive the entire lifecycle of AI Agent deployment, from concept to production and beyond for large, ambiguous, or highly complex initiatives—collaborate closely with cross‑functional teams, including product leadership and ML scientists to understand strategic needs and deliver highly effective agent solutions.
• Troubleshoot, debug, and optimize complex AI systems, ensuring exceptional performance, reliability, and scalability in production environments, and mentoring other engineers in advanced problem‑solving techniques.
• Define, establish, and continuously improve platforms and methodologies for evaluating AI agent performance, setting key metrics, driving iterative improvements across the organization, and influencing industry best practices.
• Establish and enforce best practices for documentation of development processes, architectural decisions, code, and research findings to ensure comprehensive knowledge sharing and maintainability across the team and wider engineering organization.
• Mentor and guide more junior and mid‑level developers, fostering a culture of technical excellence and continuous learning, and contributing to the growth and career development of others.

• Expert in LLM‑Oriented System Design:
  Architecting and designing complex multi‑step, tool‑using agents (e.g., Lang Chain, Autogen). Deep understanding of prompt engineering, context management, and LLM behavior quirks (e.g., hallucinations, determinism, temperature effects). Ability to implement advanced reasoning patterns like Chain‑of‑Thought and multi‑agent communication.
• Mastery of Tool Integration & APIs:
  Designing and implementing secure and scalable integrations of agents with external tools, databases, and APIs (e.g., OpenAI, Anthropic) in complex execution environments, often involving novel solutions or significant architectural considerations.
• Retrieval‑Augmented Generation (RAG):
  Designing, building, and optimizing highly performant and robust RAG pipelines with vector databases, chunking, and sophisticated hybrid search techniques.
• Leadership in Evaluation &…