Technology Architect/ Senior AI Engineer (Applied AI)
Location: Hybrid- Blaine, MN
The top three skills required for this role are:
Agentic Workflows & Memory Systems
Data & Retrieval Infrastructure
Production Reliability & Performance
Responsibilities:
Design end to end agentic AI architectures that integrate AI agent lifecycle configuration frameworks and data infrastructure into a cohesive blueprint that supports scalable enterprise solutions across multiple business domains.
Develop and refine AI agent lifecycle configuration strategies that define how agents are created, validated, deployed, monitored, and retired ensuring consistent performance and alignment with organizational objectives.
Architect modular AI agent frameworks that support reusable components, orchestration workflows, and interoperability with existing systems enabling rapid development and evolution of complex AI agent ecosystems.
Define and implement AI agent data infrastructure design including data ingestion, processing, storage, and access patterns that ensure high data quality, scalability, security, and resilience for mission critical agent operations.
Establish robust agentic AI governance structures that set clear policies, guardrails, and accountability mechanisms to manage risk, ethics, compliance, and transparency while promoting innovation across AI initiatives.
Collaborate with product engineering and operations teams to embed AI agent engineering best practices including testing, observability, versioning, and incident management to maintain reliability and trustworthiness of AI agents.
Align agentic AI solutions with hybrid work models by designing secure collaboration patterns, access controls, and workflow automations that empower distributed teams to interact effectively with AI agents in day to day operations.
Provide technical guidance on integrating AI agents with enterprise platforms and services focusing on performance optimization, fault tolerance, and maintainability so that solutions remain robust under varying workloads.
Conduct comprehensive reviews of existing AI agent implementations identifying architectural gaps and improvement opportunities then propose actionable enhancements that increase efficiency, scalability, and societal impact of AI outcomes.
Create clear architecture documentation, reference models, and design standards that help teams consistently implement agentic AI patterns while preserving flexibility for business specific customizations and innovation.
Partner with risk, security, and compliance stakeholders to assess and mitigate potential risks in AI agent behavior, data usage, and decision processes ensuring responsible deployment that benefits customers, employees, and communities.
Mentor and support technical teams by sharing expertise in AI agent engineering frameworks and data design enabling them to build high quality solutions that contribute directly to the organization purpose and long term strategy.
Evaluate emerging approaches, tools, and methodologies in agentic AI and translate relevant advances into practical architectural recommendations that keep the company competitive and positively influence industry progress.