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Senior AI Engineer, Agentic Systems
On-siteFull-timeAI EngineeringEnterprise SoftwareHuman Resources Technology
Build, evaluate, deploy, and operate production-grade LLM agents that reason over workforce and skills data. Own agent workflows from design through enterprise deployment and iteration, working with product, data, and platform teams.
Responsibilities
- Design and build multi-agent systems and orchestration, including intent routing, planning, tool use, and coordination.
- Implement retrieval and RAG pipelines over structured and unstructured workforce data.
- Integrate LLMs with tools, function calling, MCP, HCM systems, business logic, and workflows.
- Build evaluation harnesses, guardrails, safety and bias checks, governance controls, and audit trails.
- Deploy model-agnostic agents across multiple providers and collaboration platforms.
- Optimize agents for latency, cost, and reliability at enterprise scale.
- Move agents from prototype to production with monitoring, observability, and rapid iteration.
- Partner with product, data, and platform teams to turn customer needs into agent capabilities.
Requirements
- Five or more years of production software development experience.
- Proven experience building and shipping LLM agents to production.
- Hands-on experience with an agent orchestration framework such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar.
- Strong Python skills and solid software engineering fundamentals.
- Experience with prompt engineering and systematic, measurable evaluation of LLM outputs.
- Experience with tool use, function calling, and integrating LLMs with external systems.
- Experience deploying, monitoring, and maintaining AI systems in production, including cloud, CI/CD, and observability.
Nice to have
- Two or more years of hands-on LLM or generative AI experience.
- Practical experience with RAG, embeddings, and vector databases.
- Experience with MCP, agent memory, and planning or reasoning patterns.
- Background in HR technology, people data, or skills ontologies.
- Knowledge graph or graph machine learning experience.
- Experience with bias evaluation, guardrails, and AI governance.
- Experience working with multiple LLM providers.
Benefits
- Work on agentic AI used by enterprises and millions of employees.
- Solve challenging problems involving workforce data, governance, evaluation, and production reliability.
- Deploy AI capabilities inside collaboration tools used by employees.
- Join an R&D organization where AI is a core part of product development.