AI Engineering & Agentic Systems Engineer
Job in
Eden Prairie, Hennepin County, Minnesota, 55344, USA
Listed on 2026-07-01
Listing for:
eTeam Inc.
Full Time
position Listed on 2026-07-01
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Backend Developer, Cloud Engineer - Software, DevOps
Job Description & How to Apply Below
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.
Required Skills- 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)
- Lang Chain
- Lang Graph
- 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 Git Hub Actions or Cloud Build
- Cloud experience (GCP preferred, AWS acceptable)
- Vector Databases
- PostgreSQL / SQL
- MongoDB, Firestore, or other No
SQL databases - Git
- Type Script / Java Script
- Terraform or Infrastructure as Code
- Redis
- Open Telemetry
- 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 Lang Chain, Lang Graph, 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
- Lang Chain
- Lang Graph
- ClientP / FastClientP
- Vertex AI / Gemini
- AWS Bedrock
- OpenAI APIs
- RAG
- Agentic AI
- FastAPI
- AsyncIO
- Kafka
- GCP Pub/Sub
- Docker
- Kubernetes
- Git Hub Actions
- Cloud Build
- Terraform
- PostgreSQL
- MongoDB
- Firestore
- Redis
- Vector Databases
- Open Telemetry
- Grafana
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