Lead Agentic AI Architect

Archer; Jersey City (NJ); On-site; USD 150,000 - 230,000; Full time

Job summary

Archer is seeking a hands-on technical leader to define the architecture for production agentic AI solutions across our software portfolio. You will build critical components, guide engineering teams and establish reusable capabilities for AI-powered products, partnering with Product, Data, QA, Security and Platform teams to deliver reliable solutions with measurable business value.

In this role you will set portfolio architecture, lead through implementation, design dependable workflows and

Qualifications

• 10+ years in software engineering, including 4+ years leading AI and agentic architecture or technical delivery across multiple teams, or equivalent demonstrated expertise.
• Has deployed and operated multiple LLM or agentic applications in production; strong Python, APIs, distributed systems, RAG and agent orchestration skills.
• Experience with cloud deployment, containers, CI/CD, evaluation and observability; able to explain trade-offs and influence technical and business stakeholders.

Responsibilities

• Set portfolio architecture. Define reference architectures, reusable agent services and engineering standards.
• Lead through implementation. Write production code, review designs and pull requests, resolve complex technical issues and coach teams.
• Design dependable workflows. Implement tool calling, orchestration, context and memory, retries, execution limits and human approvals.
• Connect enterprise knowledge. Build Agentic, retrieval-augmented generation (RAG), structured data access and API or MCP integrations with permission-aware retrieval.
• Establish AI evaluation. Partner with QA and domain experts on test datasets, task-success measures, groundedness, tool-call accuracy and release criteria.
• Own production readiness. Implement CI/CD, versioning, monitoring, agent tracing, fallbacks and rollback; track reliability and cost.
• Embed security and governance. Apply least-privilege tool access, sensitive-data protection, prompt-injection defenses and auditability.
• Advance AI-enabled development. Govern AI-assisted engineering practices like coding agents, AI code review and test generation.
• Drive portfolio outcomes. Prioritize use cases with Product, maintain the technical roadmap and share reusable components.

Skills

Engineering leadership

Production AI delivery

Cloud & collaboration

Job description

Archer is collaborating with Verisk to connect them with exceptional professionals for this role.

Description

We are seeking a hands-on technical leader to define the architecture for, and deliver, production agentic AI solutions across our software portfolio. You will build critical components, guide engineering teams and establish reusable capabilities for AI-powered products, business workflows and AI-enabled software development, partnering with Product, Data, QA, Security and Platform teams to deliver reliable solutions with measurable business value.

Why this role:

Shape how agentic AI is applied across insurance products and workflows.

Responsibilities

• 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.

Qualifications

• 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.