AI Architect - MN/TX

Must Have Technical/Functional SkillsEnterprise 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 Scanningo Dependency Managemento Policy Compliance Validationo 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 Agento Solution Architect Agento Data Architect Agento Backend Engineering Agento Test Engineering Agento Security Review Agento 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 Copiloto Claude Codeo OpenAI Codexo 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 Lakehouseo Databricks Genieo Delta Lakeo ML/AI Pipelineso Snowflake Cortex/CoCoo Enterprise Data Governance • Enable self-service analytics, conversational AI, semantic data access, and enterprise-scale data engineering capabilities. Show more Show less