Principal AI Engineering Architect

Accountabilities

• Architecture Strategy & Technical Direction
• Define architecture patterns and technical standards for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Translate business and technology use cases into scalable solution architectures, including application design, data flows, integration patterns, model usage, security controls, and operational requirements.
• Partner with the Sr. Director, AI Platform and Engineering to shape platform architecture, technical roadmaps, reference implementations, and engineering playbooks.
• Provide hands-on architecture leadership in design reviews, technical decision-making, proof-of-concept evaluation, implementation planning, and production readiness.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, cloud AI services, enterprise knowledge systems, and secure deployment practices.

GenAI, Agentic AI & Model Engineering

• Guide implementation of Generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, RAG, semantic search, summarization, classification, extraction, and enterprise knowledge retrieval.
• Define technical patterns for Agentic AI, including tool and function calling, workflow orchestration, human-in-the-loop controls, context management, memory patterns, guardrails, monitoring, and safe execution.
• Establish usage patterns for Model Context Protocol (MCP) or similar approaches for securely connecting AI systems to enterprise tools, data sources, APIs, and workflow actions.
• Support practices for model selection, experimentation, evaluation, validation, performance monitoring, drift detection, feedback loops, and responsible production deployment.
• Help engineering teams design AI solutions that are accurate, observable, explainable where appropriate, cost-aware, and aligned with business and risk expectations.

Business Solution Architecture & Enterprise Adoption

• Partner with business, product, data, and technology teams to design AI-enabled solutions for underwriting, claims, operations, finance, customer service, and other enterprise functions.
• Translate business needs into practical AI architectures for decision support, workflow automation, document intelligence, knowledge assistance, triage, summarization, and productivity improvement.
• Create reusable patterns that allow similar AI capabilities to be deployed across multiple business processes with less rework.
• Communicate architecture decisions clearly to technical and non-technical stakeholders, including tradeoffs, risks, dependencies, and implementation options.

Security, Governance & Engineering Quality

• Partner with Security, Data, Architecture, and AI & Technology Risk Governance teams to embed secure-by-design, privacy-by-design, and responsible AI practices into solution architecture.
• Define technical controls for identity and access management, sensitive data handling, prompt and response logging, output validation, human oversight, vendor integration, and production readiness.
• Ensure AI solutions align with enterprise standards for security, privacy, auditability, observability, resilience, compliance, and operational support.
• Participate in architecture governance, design reviews, technical risk assessments, and production readiness reviews.
• Promote engineering quality through strong documentation, testability, traceability, performance considerations, and clear support models.