Artificial Intelligence Engineer

Role : AI Engineer

Location : Minneapolis MN

Need local - face to face interview required

We are looking for an experienced

AI Engineer

to design, build, and deploy enterprise-scale AI solutions using AWS Bedrock, MCP Servers, Model Gateway Architecture, and multi-agent orchestration frameworks.

📌

Key Responsibilities:

• Design, develop, and deploy enterprise-scale Agentic AI solutions using AWS AgentCore Gateway, MCP Servers, and Model Gateway architectures.
• Build and manage multi-agent systems, tool orchestration frameworks, and autonomous AI workflows.
• Develop secure MCP Server integrations connecting AI agents with enterprise systems, data sources, and business tools.
• Implement Model Gateway capabilities, including model routing, orchestration, governance, observability, and lifecycle management.
• Establish AI governance frameworks, security guardrails, access controls, compliance policies, and responsible AI practices.
• Develop scalable, production-ready AI services and orchestration frameworks using Python and cloud-native technologies.
• Lead end-to-end delivery of large-scale AI programs for Fortune 500 organizations, preferably in regulated financial services.
• Drive AI platform modernization, reusable frameworks, engineering best practices, and operational excellence.
• Provide technical leadership and mentorship to engineering teams across Agentic AI, Generative AI, and enterprise AI architecture.

✅

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or a related field.
• 8+ years of software engineering experience.
• 2+ years of hands-on experience delivering enterprise AI/GenAI solutions.
• Proven experience delivering large-scale AI implementations for Fortune 500 organizations.
• Strong expertise in:
• AWS Bedrock, AgentCore Gateway, and Knowledge Bases
• MCP Servers (Model Context Protocol)
• Model Gateway Architecture
• Agentic AI engineering and multi-agent systems
• Agent frameworks, tool orchestration, and autonomous workflows
• Python development
• Generative AI and LLM integration
• DynamoDB, Redis, and NoSQL databases
• Knowledge graphs and enterprise AI integrations

⭐

Good to Have:

• REST APIs, GraphQL, and event-driven APIs
• API management and integration platforms
• Microservices and cloud-native architecture
• Java and Spring Boot development