Senior AI Agent Engineer

About the Agentic Tribe

The Agentic Tribe is revolutionizing the chatbot and voice assistance landscape with Gen3, a cutting‑edge AI Agent system that's pushing the boundaries of conversational AI. Gen3 is not 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 are seeking a passionate and experienced AI Agent Engineer to join our team. In this role, you will be dedicated to innovating 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 will be a key player in building the cognitive architecture for our AI‑powered applications, creating systems that can reason, plan, and execute complex, multi‑step tasks.

You’ll effectively communicate complex technical concepts to both technical and non‑technical stakeholders, including those outside your immediate team.

• Design and develop robust, stateful, and scalable AI agents using Python and modern agentic frameworks (e.g., Lang Chain, Llama Index).
• Integrate AI agent solutions with existing enterprise systems, databases, and third‑party APIs to create seamless, end‑to‑end workflows.
• 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.
• Drive the entire lifecycle of AI Agent deployment—Collaborate closely with cross‑functional teams, including product managers, ML scientists, and software engineers, to understand user needs and deliver effective, high‑impact agent solutions.
• Troubleshoot, debug, and optimize complex AI systems to ensure optimal performance, reliability, and scalability in production environments.
• Establish and improve platforms for evaluating AI agent performance, defining key metrics to measure success and guide iteration.
• Document development processes, architectural decisions, code, and research findings to ensure knowledge sharing and maintainability across the team.

• LLM‑Oriented System Design:
  Designing multi‑step, tool‑using agents (Lang Chain, Autogen). Deep understanding of prompt engineering, context management, and LLM behavior quirks (e.g., hallucinations, determinism, temperature effects). Implementing advanced reasoning patterns like Chain‑of‑Thought and multi‑agent communication.
• Tool Integration & APIs:
  Integrating agents with external tools, databases, and APIs (OpenAI, Anthropic) in secure execution environments.
• Retrieval‑Augmented Generation (RAG):
  Building and optimizing RAG pipelines with vector databases, advanced chunking, and hybrid search.
• Evaluation & Observability:
  Implementing LLM evaluation frameworks and monitoring for latency, accuracy, and tool usage.
• Safety & Reliability:
  Defending against prompt injection and implementing guardrails (Rebuff, Guardrails AI) and fallback strategies.
• Performance Optimization:
  Managing LLM token budgets and latency through smart model routing and caching (Redis).
• Planning & Reasoning:
  Designing agents with long‑term memory and complex planning capabilities (ReAct, Tree‑of‑Thought).
• Programming & Tooling:
  Expert in Python, FastAPI, and LLM SDKs; experience with cloud deployment (AWS/GCP/Azure) and CI/CD for AI applications.

• Ph.D / Masters in a relevant field (e.g., Computer Science, AI, Machine Learning, NLP).
• Deep understanding of foundational ML concepts (attention, embeddings, transfer learning).
• Experience adapting academic research into production‑ready code.
• Familiarity with fine‑tuning techniques (e.g., PEFT, LoRA).