Lead - AI Engineer

About Us

vFairs is an all-in-one event management platform and Gartner Magic Quadrant Leader helping event planners power conferences, trade shows, and corporate events with in-person, hybrid, and virtual solutions. Our platform includes event registration, on-site badge printing, mobile apps, and more, managing the entire event lifecycle from planning through analytics. Rated 4.9/5 for customer support and recognized as a top platform on G2, we're known for robust technology and customer service that goes the extra mile. vFairs has a transparent, fast-paced, and collaborative culture, with geographically distributed teams united by strong processes and the autonomy to drive solutions forward.

About

About the Role

vFairs is looking for an experienced Lead AI Engineer to help us build and scale AI-powered features within our virtual events platform. You'll work at the intersection of our core product and our AI agent infrastructure — designing, building, and shipping intelligent features that make events more engaging and easier to run for organizers and attendees alike.

You'll join a small, high-leverage AI team at the center of that shift — the team responsible for the agents and AI-driven tooling behind our MCP server and support experiences today, and for the autonomous, customer-facing agents we're building next. This isn't a team bolted onto the side of the product: the agents you build will become one of the primary ways customers interact with vFairs, doing in minutes what currently takes an event organizer hours of manual setup.

What You'll Do

Technical Vision & Architecture

• Drive the vision and architecture for fully autonomous AI agents capable of planning, sequencing, and executing multi-step workflows to build out complete customer events (sessions, booths, speakers, content) with minimal human intervention.
• Establish multi-agent architectural standards using LangGraph across the engineering org, defining production-grade patterns for planner/executor models, task decomposition, sub-agent delegation, and graceful failure recovery.
• Architect and govern our MCP (Model Context Protocol) infrastructure , establishing long-term strategy, security standards, and expansion patterns for how agentic systems interface with vFairs event data.
  
  

• Define enterprise-grade safety standards , designing systemic guardrails, automated validation pipelines, and human-in-the-loop checkpoints to ensure all autonomous agent actions remain safe, reversible, and fully auditable.
• Establish company-wide evaluation frameworks to benchmark agent accuracy, drift, hallucination rates, cost, and latency, using data-driven insights to guide technical investment and iteration.
  
  

• Lead the full lifecycle of agentic features , setting best practices for moving capabilities from R&D prototypes to hardened, production-ready microservices.
• Mentor and grow the engineering team , conducting architecture reviews, setting coding standards for AI services, and raising the overall AI literacy across the department.
  
  

• Partner with Product and Executive leadership to translate high-level business vision ("build my event") into a multi-quarter AI roadmap, balancing push-the-envelope autonomy with predictability and customer trust.
• Lead cross-functional integration strategy with core engineering leads (PHP/Laravel/Vue) to seamlessly weave AI services into legacy application architectures and define how agentic actions surface in the user interface.
  
  

• 6+ years of professional software engineering experience
• Hands-on production experience building with LangGraph (or comparable agent orchestration frameworks) and FastAPI
• Experience designing multi-step or multi-agent systems — not just single-turn chat/RAG — including task planning, tool orchestration, and error recovery
• Strong Python skills, with experience designing and shipping LLM-powered applications (agents, RAG, tool-calling, structured outputs)
• Experience with MCP (Model Context Protocol) or similar tool/agent-to-application integration patterns
• Experience with vector databases (e.g., pgvector, Chroma) and SQL-based retrieval strategies
• Solid understanding of LLM application architecture: prompt design, retrieval strategies, evaluation, and handling model limitations/failure modes in production
• Judgment about autonomy: knowing when an agent should act independently versus pause for human confirmation, and how to design systems accordingly
• Comfort working across the stack — from API design to deployment — and shipping features end-to-end
• Strong communication skills and ability to work cross-functionally with product and non-AI engineering teams
  
  

• Experience with PHP, Laravel, and Vue.js — our core application stack
• Experience with multi-cloud infrastructure
• Background in building customer-facing chatbots or support automation
• Experience with WebSocket streaming for real-time AI interactions