Enterprise AI Architect-2

Eden Prairie, Minnesota 55344

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

• 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

Required Skills

Performance Architect

Job Type: Full Time

Job Category: IT

Job Description

Job Role : Enterprise AI Architect

Location : Eden Prairie, MN (Hybrid)

Job Type : Full time Permanent

Job Description

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.

Required Skills

Performance Architect