Senior AI Architect

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.

• 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 Dev Ops 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.

• 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 Sage Maker, 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.
• Enterprise architecture…