Enterprise AI Architect

Accountable for the end-to-end architecture, engineering blueprint, deployment model, operational readiness, security, governance, and integration strategy of the client's enterprise AI systems, ensuring that AI solutions operate as secure, scalable, compliant, and business-aligned systems across models, applications, infrastructure, data, networks, and external dependencies.

The core responsibilities for the job include the following:

End-to-end AI system architecture:

• Define the overall architecture of AI systems across models, agents, applications, data, APIs, infrastructure, and external services.
• Establish architecture principles, reference architectures, and technology standards.
• Define the technology selection principles and coding standards.
• Define the architecture standards to be followed by individual AI products.
• Ensure architecture supports scalability, resilience, performance, and maintainability.

Infrastructure and deployment:

• Define/approve the deployment architecture across cloud/on-premise/hybrid environments for both Maveric and client environments
• Define/approve requirements for Kubernetes, GPU infrastructure, storage, networking, and compute.
• Establish/approve deployment, release, and rollback patterns for AI systems.
• Define checklists and guidelines to ensure production-readiness of AI products.

AI engineering ecosystem development:

• Define the standardized and reusable capabilities, such as the following, that need to be consumed by all Maveric AI products in a standardized manner.
• Foundation models, AI gateways, model routing, vector databases, prompt management, RAG, guardrails, observability, and AI security.
• Define how Maveric AI platforms consume centralized platform capabilities.
• Work with the delivery and integration leader to create the reusable services.
• Determine what should be centralized as a platform capability versus embedded within individual AI products.

Business rules and AI controls:

• Ensure business rules, policies, decision logic, and human-in-the-loop controls are properly incorporated into the Maveric AI Platforms.
• Define boundaries between LLM/model behavior and deterministic business logic (i. e., what goes to the LLM vs. what is not going to the LLM).
• Define the model selection guidelines.
• Ensure AI products follow the architecture patterns in a manner in which their outputs can be controlled, validated, and audited.

Integration and external ecosystem:

• Own the architectural integration of AI systems with core banking/enterprise applications, APIs, identity platforms, data platforms, external AI/model providers, third-party services, and enterprise networks.
• Assess dependencies and architectural risks associated with external providers.

Security, risk, and compliance:

• Ensure AI architecture incorporates security and regulatory requirements.
• Define controls for data privacy, model security, prompt injection, data leakage, access control, and model abuse.
• Work with cybersecurity, risk, legal, and compliance teams to establish AI controls.
• Ensure appropriate auditability and traceability are defined and implemented.

Reliability and operations:

• Define SLOs, RTO/RPO, and operational readiness requirements.

Technology and vendor strategy:

• Evaluate AI technologies, models, platforms, and vendors.
• Define technology selection criteria and enterprise standards.
• Design standards for open-source tool stack selection.
• Approve open-source tools before they are deployed in the Maveric environment.
• Establish technology lifecycle and obsolescence strategy.

Architecture governance:

• Review and approve AI solution architectures.
• Establish architecture review checkpoints.
• Maintain enterprise AI reference architecture and standards.
• Identify and manage technical debt and architectural risks.

Requirements:

• Someone with 15+ years of experience with enterprise architecture having hands-on experience in architecting and implementing AI platforms and solutions.
• Typical skills: The role is for someone who is T-shaped, rather than an expert only in AI.
• Core: Enterprise architecture, AI governance, AI/ML architecture, generative AI/LLMs/agents, cloud and hybrid architecture, Kubernetes/container, APIs and integration, data architecture, networking, cybersecurity, DevSecOps/CI-CD, observability/SRE.