Lead AI System Architect

🕒 Junho 2

🇬🇧 Reino Unido – Remoto (RU)

⏰ Tempo Integral

🟠 Sênior

🤖 Engenheiro de IA

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EIS Ltd

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

Fundada em 2008

💳 Fintech

🤝 B2B

🛡️ Seguros

Fintech • B2B • Insurance

A EIS é a primeira escolha para seguradoras ambiciosas focadas em preparar seus negócios para o futuro e construir as plataformas de seguro centradas no cliente de amanhã. Fundada em 2008, a EIS fornece uma plataforma digital de seguros que foi especificamente projetada para remover obstáculos e proporcionar às seguradoras a liberdade de buscar e alcançar importantes objetivos estratégicos. A plataforma Coretech aberta, flexível e nativa na nuvem liberta as seguradoras para aumentar a participação no mercado, desenvolver novos produtos, criar experiências envolventes, reduzir custos de aquisição, aumentar a retenção e entregar maior receita e lucros a longo prazo. Com sede em São Francisco, a EIS atende seguradoras em todo o mundo em todas as linhas de negócios. Para mais informações, visite EISGroup.com

Descrição

• 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

• - 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) • - Flexible working hours and remote work • - Employee referral program • **Incentives and Benefits**Allowances:** • - Mobile phone and Internet allowance • **Benefits:** • - Pension on a Group Pension Scheme Basis • - Medical/Dental/Optical Health Insurance for you and your dependents • - Income Protection • - Death in Service • - Travel Insurance • ** • **[All pay components are based on objective, gender-neutral criteria within EIS’s Compensation Policy.]**

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