Senior AI Engineer

ABOUT CHUBB 

Chubb is the world's largest publicly traded property and casualty insurer. With operations in 54 countries and territories, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance, and life insurance to a diverse group of clients. The company is defined by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise, and local operations globally.

Overview

Chubb AI builds the enterprise AI platform for Chubb. The platform is a multi-tenant, model-agnostic environment that spans a web application, a desktop agent client, an AI App Store, a skills and extension ecosystem, and an API/tool layer over Chubb's knowledge.

The role

Design and build the agent infrastructure at the core of the platform: orchestration engines, the desktop agent harness, the skills and tool registries, and the policy layers that make it safe to run inside Chubb. This is a hands-on engineering role with architectural ownership, working closely with platform engineering, DevOps, security, and Model Risk Governance.

What you'll work on

• Build and extend multi-agent orchestration on Microsoft Agent Framework, LangGraph, and Deep Agents, including stateful graphs, checkpointing, sub-agents, human-in-the-loop interrupts, and long-running or background execution. 

• Build directly against frontier LLM SDKs and agent layers: Anthropic's Claude SDK and Agent SDK, OpenAI's Agents SDK and Responses API, and Google's Gemini/GenAI SDK. 

• Own the model-agnostic layer: routing across providers, adaptive routing logic, extended-thinking support, cost and token accounting, and graceful degradation as models and SDKs change. 

• Design agent capability surfaces: tool registries, agent registries, a lazy-loading skill system, and versioned skill promotion through immutable artifacts. 

• Build retrieval and grounding: document parsing, metadata-first ingestion, vector search, and end-to-end citation pipelines for documents and web results. 

• Build evaluation infrastructure: offline eval suites, regression gates on prompt and model changes, and tracing for diagnosable agent failures. 

• Engineer a meta-harness that runs multiple underlying agent runtimes behind one uniform API and event stream. 

• Build adapter layers for harnesses, normalize SDKs/event models/tool schemas/hook systems/session and permission semantics, and support a runner/server split over WebSocket. 

• Build MCP integration depth, including stdio and remote SSE servers, first-party servers such as Outlook and Atlassian, and a tool/UI bridge for sandboxed extension widgets with a manifest and permission model. 

• Implement the permission system with stateful ALLOW / ASK / DENY decisions driven by policy through hooks. 

• Work on sandboxing and process isolation, including restricted tokens, namespaces, Seatbelt/Bubblewrap-class mechanisms, container and microVM options, filesystem policy design, per-session ACLs, and platform-specific drivers. 

• Build scheduling, memory, and continuity features such as recurring tasks, cross-conversation reference, file indexing and mentions, and a runtime-agnostic persistent memory contract. 

• Apply structural patterns deliberately: facade, adapters, ports-and-adapters, factories, dependency injection, and registries. 

• Keep clean separation between business logic and infrastructure; maintain type safety, error handling, idempotency, and environment-agnostic configuration. 

• Build for operability with structured logging, OpenTelemetry tracing, Azure Application Insights, meaningful SLOs, and instrumentation for SSE streaming failures and memory-growth patterns. 

• Design multi-tenant systems with hierarchical entitlements and role models, per-user OAuth delegation (MSAL/PKCE), and rate and quota enforcement. 

• Write the design docs.