Senior Lead Architect, Agentic AI Solutions Architect

What you'll do

• Architect and build custom agentic systems for workflow automation, knowledge discovery, and service operations.
• Design prototypes, reference implementations, and reusable patterns to move AI capabilities from concept to production.
• Implement AI and agentic solution patterns in public cloud environments with a focus on scalability and observability.
• Establish reuse-first AI engineering patterns and governance across the software development lifecycle.
• Provide technical guidance and direction to business teams, contractors, and vendors regarding architecture decisions.
• Develop secure production code while reviewing and debugging code written by other team members.
• Evaluate new technologies against enterprise standards to influence product design and technical operations.
• Represent the product family in technical governance bodies to improve architecture practices.

What we're looking for

• Formal training or certification on architecture concepts and 5+ years of applied experience.
• Hands-on practical experience in system design, application development, testing, and operational stability.
• Advanced proficiency in one or more programming languages and deep expertise in software architecture and technical processes.
• Demonstrated experience applying enterprise-authorized AI capabilities within architecture and engineering workflows with a focus on data sensitivity.
• Ability to evaluate and integrate AI-enabled capabilities into enterprise-grade architectures while meeting resiliency, security, and auditability requirements.
• Hands-on experience architecting and building custom agentic systems including tool use, orchestration, memory patterns, and human-in-the-loop controls.
• Practical experience implementing AI, data, integration, or agentic solutions in public cloud environments.
• Strong understanding of LLMs, RAG, embeddings, model selection, prompt design, and responsible generative AI usage.
• Experience with vendor AI platforms and enterprise SaaS ecosystems (preferred).
• Experience operating in a regulated enterprise environment such as financial services or healthcare (preferred).
• Familiarity with security, SaaS integration, AI governance, and model risk concepts (preferred).
• Exposure to emerging agentic AI protocols like MCP, A2A, LangGraph, or CrewAI (preferred).
• Ability to mentor others and communicate complex technical trade-offs to diverse stakeholders (preferred).