Senior AI Architect

Allied Solutions • Batavia, IL

4d

About The Position

The Senior AI Architect is a senior-level strategic and technical leader responsible for shaping the enterprise-wide AI architecture vision and driving the design of scalable, ethical, and high-impact AI solutions. This role partners closely with the AI engineering team, product stakeholders, and the Office of Architecture to review requests, evaluate proof-of-concepts (POCs), establish architectural standards, and guide AI governance efforts. The Senior AI Architect ensures AI technologies are integrated into the broader enterprise ecosystem securely, responsibly, and effectively. Job Duties and Responsibilities: AI Solution Architecture & Leadership - 40% Architect end-to-end AI/ML systems including data pipelines, feature stores, training infrastructure, and inference services. Lead architectural planning for advanced use cases such as NLP, generative AI, or predictive analytics. Define best practices for AI software design, performance optimization, and deployment across cloud and on-prem environments. Set architectural direction for integrating AI with enterprise systems (e.g., APIs, message queues, core platforms). Collaborate with DevOps and MLOps teams to standardize CI/CD practices for AI models. Design Reviews, Governance & Quality - 25% Conduct technical reviews of proof of concepts, architecture diagrams, and production implementations. Evaluate AI solutions for ethical risks, regulatory compliance (e.g., GDPR, CCPA), and responsible usage. Define and enforce architectural standards for AI governance, including data retention, auditability, and explainability. Lead AI-specific architecture review boards or participate in cross-domain design councils. Identify and resolve architectural risks and technical debt in AI initiatives. Strategic Collaboration & Influence - 15% Partner with security, data, and cloud architects to align AI work with enterprise architecture. Translate business needs into scalable AI architecture blueprints. Act as an AI advisor to product teams, executives, and stakeholders. Guide prioritization of AI use cases based on feasibility, value, and strategic fit. Evangelize AI architecture principles and value propositions across business units. Innovation, Research & Continuous Learning - 10% Stay current on emerging trends (e.g., LLMs, vector databases, RAG architecture, federated learning). Evaluate new tools and frameworks for internal adoption and experimentation. Lead or sponsor innovation labs or structured pilot projects. Promote a culture of experimentation, reuse, and continuous learning within the AI team. Share technical insights and best practices through workshops, presentations, or internal documentation. Vendor & Tool Evaluation - 10% Evaluate third-party AI platforms, APIs, and infrastructure components for enterprise use. Compare costs, capabilities, and security profiles of vendor solutions. Ensure external technologies integrate cleanly with enterprise data, security, and development ecosystems. Provide architectural due diligence for AI vendor contracts and pilots. Partner with procurement or vendor management teams on technical assessments.

Requirements

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical discipline required. Master's degree preferred.
• 10+ years of software engineering or architecture experience, with at least 5 years in AI/ML architecture and solution leadership.
• Deep knowledge of AI/ML system design, including data pipelines, model lifecycle management, MLOps, and cloud-native deployments.
• Strong expertise with platforms such as Azure Machine Learning, AWS SageMaker, Google Vertex AI, Databricks, and OpenAI APIs.
• Demonstrated experience leading cross-functional teams and influencing enterprise-wide architecture decisions.
• Prior experience contributing to AI governance frameworks or responsible AI initiatives.
• Familiarity with enterprise security, data privacy laws, and risk management practices related to AI.
• Practical experience with LLM deployment, vector databases, RAG architecture, or similar emerging AI capabilities.
• Strong organizational skills and attention to detail.

Nice To Haves

• Master's degree preferred.
• Enterprise architecture certification (e.g., TOGAF, Zachman) is a plus.
• Relevant certifications such as AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, or similar credentials are preferred but not required.

Responsibilities

• Architect end-to-end AI/ML systems including data pipelines, feature stores, training infrastructure, and inference services.
• Lead architectural planning for advanced use cases such as NLP, generative AI, or predictive analytics.
• Define best practices for AI software design, performance optimization, and deployment across cloud and on-prem environments.
• Set architectural direction for integrating AI with enterprise systems (e.g., APIs, message queues, core platforms).
• Collaborate with DevOps and MLOps teams to standardize CI/CD practices for AI models.
• Conduct technical reviews of proof of concepts, architecture diagrams, and production implementations.
• Evaluate AI solutions for ethical risks, regulatory compliance (e.g., GDPR, CCPA), and responsible usage.
• Define and enforce architectural standards for AI governance, including data retention, auditability, and explainability.
• Lead AI-specific architecture review boards or participate in cross-domain design councils.
• Identify and resolve architectural risks and technical debt in AI initiatives.
• Partner with security, data, and cloud architects to align AI work with enterprise architecture.
• Translate business needs into scalable AI architecture blueprints.
• Act as an AI advisor to product teams, executives, and stakeholders.
• Guide prioritization of AI use cases based on feasibility, value, and strategic fit.
• Evangelize AI architecture principles and value propositions across business units.
• Stay current on emerging trends (e.g., LLMs, vector databases, RAG architecture, federated learning).
• Evaluate new tools and frameworks for internal adoption and experimentation.
• Lead or sponsor innovation labs or structured pilot projects.
• Promote a culture of experimentation, reuse, and continuous learning within the AI team.
• Share technical insights and best practices through workshops, presentations, or internal documentation.
• Evaluate third-party AI platforms, APIs, and infrastructure components for enterprise use.
• Compare costs, capabilities, and security profiles of vendor solutions.
• Ensure external technologies integrate cleanly with enterprise data, security, and development ecosystems.
• Provide architectural due diligence for AI vendor contracts and pilots.
• Partner with procurement or vendor management teams on technical assessments.

Benefits

• medical, dental and vision insurance coverage
• 100% company-paid life and disability coverage
• 401k options with company match
• three weeks PTO by the end of the first year

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 11-50 employees