Lead AI System Architect

The AI System Architect leads the architecture of EIS's agentic AI platform — the design of multi-agent systems that automate insurance workflows end-to-end across Policy, Billing, and Claims domains.

The role owns the patterns, frameworks, and standards for agent orchestration, MCP-based tool ecosystems, agent memory, planning, evaluation, and safety.

RAG and conversational features are table stakes; the forward agenda is autonomous and semi-autonomous agents that act on behalf of users - quote intake, claims triage, underwriting and pricing intelligence, billing troubleshooting, and beyond - across our platform and technology stacks.

Key Responsibilities

Own the architecture of EIS's agentic platform: agent orchestration, MCP-native tool ecosystems, agent memory (short-term, long-term, semantic), planning, and tool/function calling patterns reusable across product domains.

Enable and provide support for domain teams for vertical insurance agents and the horizontal capabilities (RAG, retrieval, instructional flows) they compose from.

Define and enforce levels of autonomy — assistive, semi-autonomous, autonomous — with explicit human-in-the-loop checkpoints, escalation paths, and reversibility for high-stakes actions in regulated workflows.

Drive the MCP strategy: which capabilities EIS exposes as MCP servers to internal and partner agents, how our agents consume external MCP tools, and the tool registry, schemas, and versioning that keep this scalable. Maintain the multiple stack approach as a first-class capability: Typescript, and Java.

Help teams to pick the right stack per agent and keep all aligned through shared configuration artefacts, prompt management, and evaluation tooling.

Lead Architecture Decision Records (ADRs) for agentic capabilities; partner with Platform, Security/InfoSec, and DevOps so agents are observable, testable, sandboxed, and compliant by default. Drive AI DevOps for agents: trace capture and replay, eval harnesses (task success, tool-use