G
AI Engineering Team Lead
Sin especificarTiempo completoAI EngineeringEnterprise SoftwareHR Technology
Lead and mentor an AI engineering team while remaining 50% hands-on, building production agent workflows for an enterprise workforce platform. Work closely with product, data, and design teams in a fast-paced startup environment.
Responsabilidades
- Manage, mentor, and develop a team of AI and software engineers; oversee task allocation, sprint planning, and agile ceremonies.
- Design, build, and code multi-agent systems, including intent routing, planning, tool use, and coordination.
- Set technical direction, architecture decisions, and engineering practices for scalable LLM applications grounded in a knowledge graph.
- Integrate LLMs with tools, function calling, and protocols such as MCP to enable controlled access to enterprise systems and workflows.
- Build evaluation harnesses, guardrails, and safety and bias checks; optimize latency, cost, and reliability.
- Move agents from prototype to production, including monitoring, observability, and ongoing iteration.
- Partner with product, data, and design teams to translate customer needs into capabilities, roadmaps, and priorities.
- Guide the team through rapid development cycles and changing priorities while maintaining delivery quality.
Requisitos
- At least two years of experience managing or leading a software or AI engineering team.
- At least five years of hands-on backend, AI, or software engineering experience building complex, scalable distributed systems in production.
- Hands-on experience building and shipping LLM agents to production.
- Experience with at least one agent orchestration framework, such as LangGraph, LangChain, AutoGen, CrewAI, or Semantic Kernel.
- Strong Python skills and software engineering fundamentals.
- Strong agile project management skills, including sprint planning and execution.
- Ability to work effectively in a dynamic, high-pressure startup environment with frequent changes in direction.
- Excellent communication and stakeholder collaboration skills.
Se valora
- At least two years of hands-on experience with LLMs or generative AI.
- Practical experience with RAG, embeddings, and vector databases.
- Experience with tool or function calling and external system integrations.
- Experience with MCP, agent memory, and planning or reasoning patterns.
- Knowledge of bias evaluation, guardrails, and AI governance.