Role: Enterprise AI Architect, onsite in Eden Prairie, MN/ Minnetonka, MN - Full-time
Job description: 424934/-DW
Experience Required: 10 - 24+ Years
Must Have Technical/Functional Skills
Enterprise AI Architect with Full Development Experience (FDE), possessing deep expertise in architecture, hands-on software engineering, AI-assisted development, Agentic AI frameworks, DevSecOps, platform engineering, cloud-native solutions, and enterprise data platforms. Proven ability to architect, develop, secure, automate, and operationalize large-scale AI and software solutions while driving engineering excellence through GitHub Copilot, Claude Code, Codex, Databricks Genie, Snowflake Cortex, and modern AI-powered software delivery practices.
Key Responsibilities
- Enterprise AI & Solution Architecture
- Lead the architecture, design, and implementation of enterprise-scale AI solutions using modern architectural patterns, clean architecture principles, domain-driven design (DDD), and cloud-native technologies.
- Define enterprise AI reference architectures, engineering standards, development frameworks, and implementation guardrails to ensure scalability, maintainability, security, and operational excellence.
- Drive adoption of Agentic AI, AI-powered software engineering, and intelligent automation across the software delivery lifecycle.
- Architect solutions with built-in observability, resilience, governance, security, and compliance from inception through production deployment.
- Partner with business, engineering, security, and platform teams to align AI capabilities with enterprise technology strategy and business outcomes.
- Full Development Experience (FDE) and Engineering Excellence
- Demonstrate hands-on full-stack development experience spanning frontend, backend, APIs, data platforms, cloud services, and AI-enabled applications.
- Lead development teams in implementing modern engineering practices including test-driven development (TDD), CI/CD automation, code quality enforcement, and platform engineering standards.
- Define and enforce software engineering best practices with mandatory automated test coverage, code reviews, architecture reviews, and deployment quality controls.
- Drive modernization of legacy applications through refactoring, cloud migration, microservices transformation, and AI-assisted development methodologies.
- Establish engineering productivity frameworks leveraging AI coding assistants, automated development workflows, and intelligent code generation.
- Secure-by-Design AI Platforms
- Architect secure AI and software platforms aligned with OWASP standards, Zero Trust principles, and enterprise cybersecurity requirements.
- Implement enterprise controls for HIPAA, PHI, PII, GDPR, and regulatory compliance across data, applications, and AI workloads.
- Integrate security validation throughout the development lifecycle using SAST, SCA, container scanning, secrets management, and policy-as-code frameworks.
- Design auditable AI systems with governance, lineage, traceability, access controls, and compliance monitoring capabilities.
- AI Engineering, DevSecOps, and Delivery Automation
- Design and implement AI Engineering Harnesses supporting build validation, quality gates, security scanning, automated testing, and deployment automation.
- Establish enterprise DevSecOps frameworks integrating:
- Static Application Security Testing (SAST)
- Software Composition Analysis (SCA)
- Container Security Scanning
- Dependency Management
- Policy Compliance Validation
- Infrastructure-as-Code Governance
- Lead implementation of performance benchmarking frameworks for APIs, AI models, applications, and distributed platforms.
- Build highly automated CI/CD pipelines enabling secure, reliable, and repeatable software delivery.
- Agentic AI Development Frameworks
- Design and operationalize multi-agent software engineering ecosystems to accelerate architecture, development, testing, security review, and governance activities.
- Utilize specialized AI agents including:
- Enterprise Architect Agent
- Solution Architect Agent
- Data Architect Agent
- Backend Engineering Agent
- Test Engineering Agent
- Security Review Agent
- Pull Request Review Agent
- Drive adoption of agent-based development workflows to improve engineering productivity, software quality, and delivery velocity.
- AI-Assisted Software Engineering Toolchain
- Extensive hands-on experience using:
- Visual Studio Code with GitHub Copilot
- Claude Code
- OpenAI Codex
- Enterprise AI coding assistants
- Leverage repository-wide reasoning, large-scale codebase analysis, architecture discovery, code modernization, and AI-assisted implementation patterns.
- Architect AI-powered developer experiences integrating intelligent code review, automated remediation, documentation generation, and engineering workflow automation.
- Data & AI Platform Architecture
- Design and implement scalable data and AI platforms leveraging Databricks, Snowflake, cloud-native services, and modern data architectures.
- Experience with:
- Databricks Lakehouse
- Databricks Genie
- Delta Lake
- ML/AI Pipelines
- Snowflake Cortex/CoCo
- Enterprise Data Governance
- Enable self-service analytics, conversational AI, semantic data access, and enterprise-scale data engineering capabilities.
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