Lead Agentic AI Architect

• Why this role: Shape how agentic AI is applied across insurance products and workflows
• Set portfolio architecture. Define reference architectures, reusable agent services and engineering standards. Select models, frameworks and build-versus-buy options based on quality, cost and business needs.
• Lead through implementation. Write production code, review designs and pull requests, resolve complex technical issues and coach teams through delivery and adoption.
• Design dependable workflows. Implement tool calling, orchestration, context and memory, retries, execution limits and human approvals. Choose simple automation, a single agent or multiple agents as the problem warrants.
• Connect enterprise knowledge. Build Agentic, retrieval-augmented generation (RAG), structured data access and API or Model Context Protocol (MCP) integrations with permission-aware retrieval and source traceability.
• Establish AI evaluation. Partner with QA and domain experts on test datasets, task-success measures, groundedness, tool-call accuracy, adversarial testing and release acceptance criteria.
• Own production readiness. Implement CI/CD, versioning, monitoring, agent tracing, fallbacks and rollback. Track reliability, latency and cost per successful task, and support incident resolution.
• Embed security and governance. Apply least-privilege tool access, sensitive-data protection, prompt-injection defenses, auditability and approval controls with Security and governance teams.
• Advance AI-enabled development. Introduce and govern AI-assisted engineering practices such as coding agents, AI code review and test generation, and measure their effect on delivery speed and quality.
• Drive portfolio outcomes. Prioritize use cases with Product, maintain the technical roadmap and share reusable components and lessons across teams to improve delivery speed and customer value.
• Engineering leadership. 10+ years in software engineering, including 4+ years leading AI and agentic architecture or technical delivery across multiple teams, or equivalent demonstrated expertise.
• Production AI delivery. Has deployed and operated multiple LLM or agentic application in production; strong Python, APIs, distributed systems, RAG and agent orchestration skills.
• Cloud and collaboration. Experience with cloud deployment, containers, CI/CD, evaluation and observability; able to explain trade-offs and influence technical and business stakeholders.
• AWS and Amazon Bedrock, including AgentCore; LangGraph, Stands or comparable agent frameworks; MCP; semantic layers, ontologies or knowledge graphs; insurance or other regulated data products; AI-enabled SDLC practices.
• We are seeking a hands-on technical leader to define the architecture for, and deliver, production agentic AI solutions across our software portfolio.

None flagged.