Technology Architect / Senior AI Engineer (Applied AI)

Job Title: Technology Architect / Senior AI Engineer (Applied AI)



Location: Hybrid Blaine, MN



Experience Required: 10 14 years



Work Model: Hybrid



Shift: Day



Travel Required: No

Job Summary

Drive enterprise-grade agentic AI architecture for a global organization by designing scalable agent frameworks, data infrastructure, and governance models that power secure and reliable AI agents in a hybrid work setup. Collaborate with cross-functional teams to configure AI agent lifecycles and ensure robust alignment with business objectives while maintaining strong compliance and ethical standards.

Top 3 Skills Required

1. Agentic Workflows and Memory Systems
2. Data and Retrieval Infrastructure
3. Production Reliability and Performance

1. Agentic Workflows and Memory Systems

• Stateful Orchestration: Building and debugging production-grade, cyclic multi-agent workflows and state machines.
• Context and Memory Engineering: Implementing multi-layered memory architectures including short-term memory for active turn execution, long-term memory for cross-session state persistence, and episodic summarization to manage token context windows.
• Tool Call Management: Designing dependable function-calling patterns equipped with automated retry logic and self-correction handlers.

2. Data and Retrieval Infrastructure

• Vector and Relational Storage: Managing relational metadata schemas and executing optimized semantic vector similarity searches within a combined relational database layer.
• Document Persistence: Utilizing document-store databases to store unstructured execution payloads, dynamic agent states, and raw chat logs.
• Hybrid RAG Pipelines: Combining relational/exact-match queries with vector-space searches for high-precision retrieval.

3. Production Reliability and Performance

• Granular Tracing: Instrumenting end-to-end tracing to monitor agent execution steps, debug non-deterministic loops, and track token costs.
• Automated Evals: Creating programmatic evaluation testing and scoring frameworks to benchmark agent accuracy before production deployment.
• Core Backend Development: Writing clean, concurrent, asynchronous Python code to handle high-throughput foundation model APIs.

Responsibilities

• Design end-to-end agentic AI architectures that integrate AI agent lifecycle configuration frameworks and data infrastructure into a cohesive blueprint supporting 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 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.
• Mentor and support technical teams by sharing expertise in AI agent engineering frameworks and data design, enabling them to build high-quality solutions aligned with long-term strategy.
• Evaluate emerging approaches, tools, and methodologies in agentic AI and translate relevant advances into practical architectural recommendations.

Qualifications

• Extensive experience in AI agent lifecycle configuration, with the ability to describe and implement practical processes for designing, monitoring, and iterating on agent behavior in complex environments.
• Strong hands-on expertise with AI agent frameworks and AI agent engineering, applying structured design patterns and implementation techniques to deliver robust, scalable, and maintainable agent-based solutions.
• Deep proficiency in AI agent data infrastructure design, including event-driven architectures, metadata management, and secure data pipelines that sustain reliable operation of advanced agentic AI systems.
• Solid understanding of agentic AI governance principles and practices, with experience translating ethical, regulatory, and organizational requirements into actionable controls and standards for AI agents.

Certifications

Preferred certifications include AI architecture or advanced machine learning credentials, such as a TOGAF AI specialization or an equivalent industry-recognized AI engineering certification.