Staff AI Agent Engineer

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 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.

About the Role:

We're seeking a highly experienced and influential Staff 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.

What You will Do (Responsibilities):

• Architect, design, and lead the development of robust, stateful, and scalable AI agents using Python and modern agentic frameworks (e.g., LangChain, LlamaIndex), 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, 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.

Core Technical Competencies:

• Expert in LLM-Oriented System Design: Architecting and designing complex multi-step, tool-using agents (e.g., LangChain, 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 & Observability: Defining, implementing LLM evaluation frameworks and comprehensive monitoring for latency, accuracy, and tool usage across production systems, influencing the observability strategy.

• Safety & Reliability: Designing and implementing state-of-the-art defenses against prompt injection and robust guardrails (e.g., Rebuff, Guardrails AI) and complex fallback strategies.

• Performance Optimization: Deep expertise in managing LLM token budgets and latency through smart model routing, caching (e.g., Redis), and other advanced optimization techniques, identifying and addressing systemic performance bottlenecks.

• Planning & Reasoning: Designing and implementing cutting-edge agents with long-term memory and highly complex planning capabilities (e.g., ReAct, Tree-of-Thought)

• Programming & Tooling: Expert in Python, FastAPI, and LLM SDKs; extensive experience and strategic contributions with cloud deployment (AWS/GCP/Azure) and CI/CD for complex AI applications.

Bonus Points (Preferred Qualifications)

• Ph.D / Masters in a relevant field (e.g., Computer Science, AI, Machine Learning, NLP).

• Comprehensive 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).