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:
o Static Application Security Testing (SAST)
o Software Composition Analysis (SCA)
o Container Security Scanning
o Dependency Management
o Policy Compliance Validation
o 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:
o Enterprise Architect Agent
o Solution Architect Agent
o Data Architect Agent
o Backend Engineering Agent
o Test Engineering Agent
o Security Review Agent
o 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:
o Visual Studio Code with GitHub Copilot
o Claude Code
o OpenAI Codex
o 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:
o Databricks Lakehouse
o Databricks Genie
o Delta Lake
o ML/AI Pipelines
< div>o Snowflake Cortex/CoCo
o Enterprise Data Governance
• Enable self-service analytics, conversational AI, semantic data access, and enterprise-scale data engineering capabilities.