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Gloat is the AI-native platform powering the era of Agentic HR. Powered by Loomra, our context engine is purpose-built for the workforce. Gloat lets you build any Agentic HR agent that operates in the flow of work across Slack, Microsoft Teams, Copilot, and Google Chat. Talent Redeployment, internal mobility, skills intelligence, workforce redesign, and whatever comes next.
Trusted by leading global enterprises, Gloat serves over 1.5 million employees across 30+ Fortune 500 companies, unlocking more than 4.8 million strategic work hours. We help organizations harness AI to unlock agility, productivity, and growth, empowering them to achieve true ROI and drive exponential productivity.
We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership.
As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level.
You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers.
What you'll do
Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies.
Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph.
System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks.
Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment.
Requirements
2+ years of experience managing or leading a high-performing software/AI engineering team.
5+ years of hands-on experience as a Backend or AI/Software Engineer building complex, scalable distributed systems in production.
Proven hands-on experience building and shipping LLM agents to production - not just demos or prototypes.
Hands-on with at least one agent orchestration framework (e.g. LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar).
Strong Python and solid software engineering fundamentals.
Strong agile project management skills with a track record of effective sprint planning and execution.
Demonstrated ability to thrive in a dynamic, high-pressure startup environment with rapid execution and frequent shifts in direction.
Excellent communication skills, with the ability to build strong partnerships with Product Managers and other stakeholders.
Nice to have
2+ years hands-on with LLMs / generative AI.
Practical experience with RAG, embeddings, and vector databases (e.g. pgvector, Pinecone, or similar).
Experience with tool use / function calling and integrating LLMs with external systems.
Experience with MCP, agent memory, and planning/reasoning patterns.
Responsible AI: bias evaluation, guardrails, and AI governance.