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 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. Skills, Knowledge & Expertise - 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. buy, vendor vs. in-house, agent vs. deterministic workflow, model choice, and total cost of ownership. - Excellent written and verbal communication; able to make agentic trade-offs accessible to non-AI audiences. - Advanced degree in Computer Science, AI/ML, or a related field - or equivalent practical experience. Job Benefits - Work with top talent and great colleagues who are industry and technology experts. Operate in a Scaled Agile environment, diverse, multicultural and cross-functional teams - We are a global and modern software product company building world-class Enterprise InsurtTech Product powered by leading-edge technologies (microservices, reactive, cloud, continuous delivery) - We work with the newest Apple Macbooks - We offer freedom - build from building your career path through development programs and exciting global mobility opportunities (we have a global culture) Incentives and Benefits: Allowances: - Homeworking Equipment - Mobile phone/internet coverage Benefits: - Health Insurance for you and your dependents - eligible immediately upon joining EIS - Sport - Optional choice in Health insurance package - Pension on a Group Pension Scheme Basis [All pay components are based on objective, gender-neutral criteria within EIS’s Compensation Policy.] Originally posted on Himalayas