Lead AI Solutions Architect (Lead AI Engineer)

SchoolsFirst Federal Credit Union • Sacramento, CA

1d

About The Position

The Lead AI Solutions Architect (Lead AI Engineer) leads the design, delivery, and continuous improvement of complex AI, automation, and agentic solutions. The role establishes technical standards, drives enterprise integrations, guides implementation through production deployment, and mentors teams to advance engineering quality, scalability, and business value.

The position owns technical delivery of AI and automation initiatives from proof of concept through production deployment and ongoing support. It creates reusable reference implementations, components, and engineering patterns for knowledge assistants, RAG solutions, agents, tool and API integrations, human-in-the-loop controls, and citizen developer capabilities. The role defines how stand-alone and embedded RPA workflows and AI agents fit within enterprise architecture and leads integrations across enterprise applications, data services, APIs, and approved AI and orchestration platforms.

The architect establishes engineering practices for version control, CI/CD, evaluation, regression testing, and release management. The role defines and validates nonfunctional requirements; implements approved data access, identity, least-privilege, and governance controls with partner teams; and leads technical design, code, testing, and production-readiness reviews. It also establishes operational monitoring and incident response practices, evaluates emerging AI and automation technologies, and coordinates and mentors teams to promote reusable engineering practices.

The position requires a high school diploma or GED and a bachelor's degree in a related field or equivalent years of experience, along with 7–10 years of relevant experience. Several AI and cloud certifications are preferred.

Requirements

• High school diploma or GED.
• Bachelor's degree in a related field or equivalent years of experience.
• 7–10 years of prior relevant experience.
• Demonstrated experience in one or more of traditional AI, machine learning, generative AI, or agentic AI, including hands-on solution design, implementation, and production deployment.
• Advanced knowledge of enterprise solution architecture, cloud platforms, systems integration, and distributed application design.
• Demonstrated expertise designing API-driven, event-driven, and AI-enabled solutions for enterprise scalability, security, and operational effectiveness.
• Advanced knowledge of generative AI, LLMs, RAG, AI agents, intelligent automation, RPA platforms, orchestration platforms, and low-code development technologies.
• Strong knowledge of identity and access management, security architecture, data governance, privacy requirements, and zero-trust principles.
• Knowledge of enterprise data architecture, integration patterns, observability frameworks, and operational support considerations.
• Demonstrated ability to evaluate technologies, vendors, and architectural approaches and recommend options aligned with business needs and enterprise standards.
• Ability to establish technical standards, reference architectures, design patterns, and governance practices that promote consistency, scalability, and maintainability.
• Ability to lead cross-functional solution design efforts, influence technical decisions, and communicate architectural concepts and tradeoffs to technical and business stakeholders.

Nice To Haves

• Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103).
• Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300).
• Microsoft Certified: AI Agent Builder Associate (AB-620).
• AWS Certified Generative AI Developer – Professional.

Responsibilities

• Lead the design, development, integration, testing, deployment, and operation of complex AI-enabled workflows, solutions, agents, and shared platform capabilities.
• Own technical delivery for AI and automation initiatives from proof of concept through production deployment and ongoing support.
• Create reusable reference implementations, components, and engineering patterns for knowledge assistants, RAG solutions, agents, tool and API integrations, human-in-the-loop controls, and citizen developer capabilities.
• Define the role of stand-alone and embedded RPA workflows and AI agents within enterprise architecture, and guide engineers in designing secure, scalable, and maintainable solutions.
• Lead integrations across enterprise applications, data services, APIs, and approved AI and orchestration platforms.
• Establish and enhance engineering practices for version control, CI/CD, evaluation frameworks, regression testing, and release management across AI, agent, and workflow solutions.
• Define and validate nonfunctional requirements, including reliability, scalability, performance, observability, auditability, and supportability.
• Implement approved data access, identity, least-privilege, and governance controls in partnership with Architecture, Information Security, and Data teams.
• Lead technical design, code, testing, and production-readiness reviews; resolve complex implementation issues and provide escalation support.
• Establish monitoring, logging, alerting, operational dashboards, runbooks, and incident response procedures to improve performance, resiliency, and supportability.
• Evaluate AI, automation, and agentic technologies through technical proofs of value and recommend platform, vendor, and solution approaches.
• Provide thought leadership regarding emerging AI, automation, and digital transformation technologies.
• Mentor engineers, coordinate delivery across teams, and promote reusable engineering practices that improve solution quality, consistency, and delivery effectiveness.
• Perform other duties as assigned.
• Comply with regulatory compliance and assigned training requirements, including applicable BSA regulations.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior