AI Engineering & Agentic Systems Engineer

Location: Eden Prairie, MN 55344
Work Model: Hybrid
Duration: 6 Months Contract
Experience Required: 10+ Years

We are looking for a hands-on AI Engineering & Agentic Systems Engineer with experience building production-grade AI applications using Large Language Models (LLMs) and multi-agent systems. This is an engineering role focused on developing scalable AI solutions, not a traditional Machine Learning or Data Science position.

• 10+ years of software engineering experience
• Strong Python programming experience
• Hands-on experience building production LLM-powered applications
• Experience with Agentic AI frameworks and tools:
  • Google Agent Development Kit (ADK)
  • LangChain
  • LangGraph
  • Model Context Protocol (ClientP/FastClientP or similar)
  • A2A / ACP agent communication protocols
• Experience with LLM APIs:
  • Vertex AI / Gemini
  • AWS Bedrock
  • OpenAI
• Experience with Retrieval-Augmented Generation (RAG)
• Multi-Agent orchestration
• Function Calling and Structured Outputs
• Human-in-the-Loop (HITL) workflows
• FastAPI and AsyncIO
• REST API development
• Apache Kafka or GCP Pub/Sub
• Docker and Kubernetes
• CI/CD using GitHub Actions or Cloud Build
• Cloud experience (GCP preferred, AWS acceptable)
• Vector Databases
• PostgreSQL / SQL
• MongoDB, Firestore, or other NoSQL databases
• Git

• TypeScript / JavaScript
• Terraform or Infrastructure as Code
• Redis
• OpenTelemetry
• Grafana
• Open Policy Agent (OPA)
• SPIFFE / Workload Identity
• Prompt management and evaluation tools
• Responsible AI and AI governance
• AI observability and monitoring
• Healthcare or Insurance domain experience

• Design and develop AI-powered applications using LLMs and Agentic AI frameworks.
• Build multi-agent workflows using LangChain, LangGraph, and Google ADK.
• Develop scalable RAG-based applications with Vector Databases.
• Integrate enterprise systems using REST APIs, Kafka, and cloud services.
• Build reusable AI platform components for prompt orchestration, agent frameworks, and AI workflows.
• Develop production-ready AI services using Python, FastAPI, Docker, and Kubernetes.
• Implement monitoring, logging, evaluation, and governance for AI applications.
• Build automated CI/CD pipelines and deploy applications in GCP or AWS.
• Work closely with cross-functional teams to deliver enterprise AI solutions.
• Mentor developers and promote AI-first engineering best practices.

• Python
• Google ADK
• LangChain
• LangGraph
• ClientP / FastClientP
• Vertex AI / Gemini
• AWS Bedrock
• OpenAI APIs
• RAG
• Agentic AI
• FastAPI
• AsyncIO
• Kafka
• GCP Pub/Sub
• Docker
• Kubernetes
• GitHub Actions
• Cloud Build
• Terraform
• PostgreSQL
• MongoDB
• Firestore
• Redis
• Vector Databases
• OpenTelemetry
• Grafana