Sr. Enterprise AI Architect [18+yrs]

Accountable for the end-to-end architecture, engineering blueprint, deployment model,
operational readiness, security, governance and integration strategy of 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.
That means the architect will have visibility across the entire chain:
Business → Business Rules → AI Platform → Models → Data/Knowledge grpahs →
APIs/Integration → Network → Infrastructure → Security → Deployment → Operations →
Monitoring
Key responsibilities
1. 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
 Establish
 Ensure architecture supports scalability, resilience, performance and maintainability.
2. Infrastructure & deployment
 Define/ Approve the deployment architecture across cloud/ on-premise/ hybrid
environments both for 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.
3. AI engineering ecosystem development
 Define the standardized and reusable capabilities such as the following that needs to
be consumed by all Maveric AI products in a standardized manner
o Foundation models
o AI gateways
o Model routing
o Vector databases
o Prompt management
o RAG
o Guardrails
o Observability
o AI security
 Define how Maveric AI platforms consume centralized platform capabilities
 Work with Delivery and Integration leader to create the reusable services
 Determine what should be centralized as a platform capability versus embedded
within individual AI Products.
4. Business rules & 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 behaviour and deterministic business logic
(i.e what goes to LLM vs. What is not going to LLM)
 Define the model selction guidelines
 Ensure AI products follows the architecture patterns in a manner its outputs can be
controlled, validated and audited.
5. Integration & external ecosystem
 Own the architectural integration of AI systems with:
o Core banking / enterprise applications
o APIs
o Identity platforms
o Data platforms
o External AI/model providers
o Third-party services
o Enterprise networks
 Assess dependencies and architectural risks associated with external providers.
6. Security, risk & 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 is defined and implemented
7. Reliability & operations
 Define SLOs, RTO/RPO and operational readiness requirements.
8. Technology & vendor strategy
 Evaluate AI technologies, models, platforms and vendors.
 Define technology selection criteria and enterprise standards.
 Degine standards for opensource tool stack selection
 Approve opensource tools before they are deployed in Maveric environment
 Establish technology lifecycle and obsolescence strategy.
9. 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.
Experience Range
Someone with 20+ yrs of experience with enterprise architecture having hands-on
experience in architecting and Implementing AI platforms and solutions
Typical Level – AVP
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 & hybrid architecture
 Kubernetes / containers
 APIs & integration
 Data architecture
 Networking
 Cybersecurity
 DevSecOps / CI-CD
 Observability / SRE

Work arrangement
No