Senior AI Agent Engineer (Machine Learning)

<h2>Job Description</h2><p></p><h2>About The Agentic Tribe:</h2><p><span>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.</span></p><p></p><h2>About the Role:</h2><p><span>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 </span>designing, developing, and deploying intelligent, autonomous agents<span> 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 </span>communicate complex technical concepts to both technical and non-technical stakeholders, including those outside your immediate team.</p><p></p><h2>What You will do (Responsibilities):</h2><ul><li><p><span>Design and develop robust, stateful, and scalable AI agents using Python and modern agentic frameworks (e.g., LangChain, LlamaIndex).</span></p></li><li><p><span>Integrate AI agent solutions with existing enterprise systems, databases, and third-party APIs to create seamless, end-to-end workflows.</span></p></li><li><p><span>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.</span></p></li><li><p><span>Drive the entire lifecycle of AI Agent <span>deployment—Collaborate</span> closely with cross-functional teams, including product managers, ML scientists, and software engineers, to understand user needs and deliver effective, high-impact agent solutions.</span></p></li><li><p><span>Troubleshoot, debug, and optimize complex AI systems to ensure optimal performance, reliability, and scalability in production environments.</span></p></li><li><p><span>Establish and improve platforms for evaluating AI agent performance, defining key metrics to measure success and guide iteration.</span></p></li><li><p><span>Document development processes, architectural decisions, code, and research findings to ensure knowledge sharing and maintainability across the team.</span></p></li></ul><p></p><h2>Core Technical Competencies:</h2><ul><li><p>LLM-Oriented System Design:<span> Designing multi-step, tool-using agents (LangChain, 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.</span></p></li><li><p>Tool Integration &amp; APIs:<span> Integrating agents with external tools, databases, and APIs (OpenAI, Anthropic) in secure execution environments.</span></p></li><li><p>Retrieval-Augmented Generation (RAG):<span> Building and optimizing RAG pipelines with vector databases, advanced chunking, and hybrid search.</span></p></li><li><p>Evaluation &amp; Observability:<span> Implementing LLM evaluation frameworks and monitoring for latency, accuracy, and tool usage.</span></p></li><li><p>Safety &amp; Reliability:<span> Defending against prompt injection and implementing guardrails (Rebuff, Guardrails AI) and fallback strategies.</span></p></li><li><p>Performance Optimization:<span> Managing LLM token budgets and latency through smart model routing and caching (Redis).</span></p></li><li><p>Planning &amp; Reasoning:<span> Designing agents with long-term memory and complex planning capabilities (ReAct, Tree-of-Thought).</span></p></li><li><p>Programming &amp; Tooling:<span> Expert in Python, FastAPI, and LLM SDKs; experience with cloud deployment (AWS/GCP/Azure) and CI/CD for AI applications.</span></p></li></ul><p>Bonus Points (Preferred Qualifications):</p><ul><li><p><span>Ph.D / Masters in a relevant field (e.g., Computer Science,  AI, Machine Learning, NLP).</span></p></li><li><p><span>Deep understanding of foundational ML concepts (attention, embeddings, transfer learning).</span></p></li><li><p><span>Experience adapting academic research into production-ready code.</span></p></li><li><p><span>Familiarity with fine-tuning techniques (e.g., PEFT, LoRA).</span></p></li></ul><p></p><h2>The Interview Process:</h2><p><span>We are excited to learn more about you, so we want to be transparent about what you can expect from our interview process:</span><br/> </p><p><span>1. Initial Call