AI Architect

Siri InfoSolutions Inc; Eden Prairie (MN); On-site; USD 150,000 - 190,000; Full time

Job summary

Siri InfoSolutions Inc in Eden Prairie, MN seeks an Enterprise AI Architect with deep hands-on development and AI-enabled software delivery expertise to design scalable platforms and secure architectures.

You will drive full development lifecycle, implement DevSecOps, agentic AI frameworks, and data platform architectures using Databricks, Snowflake Cortex, and cloud-native services.

Qualifications

• Extensive experience architecting enterprise AI platforms and large-scale software systems.
• Hands-on software engineering across frontend, backend, APIs, and data platforms.
• Proven ability to secure, automate, and operationalize AI-enabled solutions.

Responsibilities

• Lead architecture, design, and implementation of enterprise-scale AI solutions with cloud-native patterns.
• Define reference architectures, standards, and guardrails for scalable AI delivery.
• Drive AI engineering, DevSecOps, and automated testing across the lifecycle.
• Architect secure platforms with governance, privacy, and compliance in mind.
• Design multi-agent AI development ecosystems and agent-based workflows.
• Develop AI toolchains and developer experiences with intelligent automation.
• Plan data and AI platform architectures using Databricks, Snowflake Cortex, and related tech.

Skills

Enterprise AI Architect

Full Development Experience

AI-assisted development

Agentic AI frameworks

DevSecOps

Platform engineering

Cloud-native solutions

Enterprise data platforms

Tools

GitHub Copilot

Claude Code

Codex

Databricks Genie

Snowflake Cortex

Job description

Role: Enterprise AI Architect
Location- Eden Prairie, MN
Hire Type: Full time

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
• < div>o Snowflake Cortex/CoCo
• Enterprise Data Governance