Technology Architect / Senior AI Engineer (Applied AI)

Hi,

Our client is looking for Technology Architect / Senior AI Engineer (Applied AI) in Blaine, MN location below is the detailed requirements,

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

Location: Blaine, MN

Job Description:

• Bachelor's degree in related field.

Agentic Workflows & Memory Systems:

• Stateful Orchestration: Building and debugging production-grade, cyclic multi-agent workflows and state machines.
• Context & 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.

Data & Retrieval Infrastructure

• Vector & Relational Storage: Managing relational metadata schemas and executing optimized semantic vector similarity searches inside 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.

Production Reliability & 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
• 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.

Qualifications:

• Demonstrate 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.
• Possess 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.
• Show 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.
• Exhibit 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.