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.

Skills
AI, AI platform, Artificial Intelligence, Chief Architect, Enterprise Architect, GenAI, Generative AI, Kubernetes, LLM, Principal Architect, Solutions Architect, hybrid cloud

Experience
12-15 yrs