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.

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 correctness, regression), prompt and model versioning, cost and latency budgets per agent, and progressive rollout strategies. Set safe-AI standards for agentic systems: prompt injection and tool-poisoning defenses, action allow-lists, blast-radius controls, PII handling, data residency, and bias mitigation.

Treat agent safety as a first-class architectural concern. Translate insurance use cases into production agent designs with product strategists and domain architects; provide technical leadership and mentorship; communicate agentic trade-offs (autonomy, reliability, cost, safety) clearly to executives, customers, and engineers. Proven track record designing and shipping agentic systems in production - not demos, not prototypes - with meaningful autonomy and multi-step tool use.

Strong systems background: data-intensive, distributed, and latency-sensitive design in production environments. Deep, hands-on experience with agent patterns: orchestration, planning, ReAct-style and graph-based agents, agent memory, tool/function calling, MCP, structured outputs. Sharp instinct for when an agent is the right answer and when a deterministic workflow is.

Tracks the frontier and translates what matters into the roadmap. Strong with the Java/Spring ecosystem. Strong with Typescript and Python for AI (LangChain, LangGraph, or equivalent agent framework) - production experience required.

Equally comfortable in both stacks. Hands-on with vector databases including embedding models, hybrid search, re-ranking, and retrieval evaluation. Experience with agent evaluation and observability: traces, replays, eval harnesses, guardrails, and cost/latency telemetry.

Familiar with AI configuration-as-code. Experience shipping AI services on cloud platforms (AWS, Azure, GCP) in regulated enterprise environments - security review, data residency, audit trails. Familiarity with insurance, financial services, or another regulated domain is a plus.

Strong architectural judgment — pragmatic about build vs.