Enterprise AI Architect

About This Role India's BFSI sector is running a 42 percent skills gap in AI and data roles, and organisations are offering 1.5x to 2.5x salary premiums to attract the specialists who can close it (People Matters). This role sits at the centre of that gap. The Enterprise AI Architect is 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 those systems operate as secure, scalable, compliant, and business-aligned across models, applications, infrastructure, data, networks, and external dependencies.

That means visibility across the entire chain: Business, Business Rules, AI Platform, Models, Data and Knowledge Graphs, APIs and Integration, Network, Infrastructure, Security, Deployment, Operations, and Monitoring.

What You Will Own

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 technology selection principles and coding standards, and the architecture standards followed by individual AI products
• Ensure architecture supports scalability, resilience, performance, and maintainability

Infrastructure and Deployment

• Define and approve the deployment architecture across cloud, on-premise, and hybrid environments, both for the client's own environment and its enterprise customers' environments
• Define and approve requirements for Kubernetes, GPU infrastructure, storage, networking, and compute
• Establish and 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 standardised, reusable capabilities consumed by all of the client's AI products, including foundation models, AI gateways, model routing, vector databases, prompt management, RAG, guardrails, observability, and AI security
• Define how the client's AI platforms consume centralised platform capabilities, working with the Delivery and Integration leaders to create the reusable services
• Determine what should be centralised as a platform capability versus embedded within individual AI products

Governance, Security and Integration

Business Rules and AI Controls

• Ensure business rules, policies, decision logic, and human-in-the-loop controls are properly incorporated into the client's AI platforms
• Define the boundary between LLM or model behaviour and deterministic business logic, what goes to the model and what does not
• Define model selection guidelines
• Ensure AI products follow architecture patterns whose outputs can be controlled, validated, and audited

Integration and External Ecosystem

• Own the architectural integration of AI systems with core banking and enterprise applications, APIs, identity platforms, data platforms, external AI and 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 is defined and implemented

Reliability, Technology and Architecture Governance

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, including standards for open-source tool stack selection
• Approve open-source tools before they are deployed in the client's environment
• Establish technology lifecycle and obsolescence strategy

Architecture Governance

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

What You Need to Succeed

12-15 years of enterprise architecture experience, with hands-on experience architecting and implementing AI platforms and solutions. Typical level: AVP. The role is for someone who is T-shaped, broad enterprise architecture and governance depth, not an expert only in AI.

• Enterprise architecture and AI governance
• AI/ML architecture and Generative AI, LLMs, and agents
• Cloud and hybrid architecture, Kubernetes and containers
• APIs and integration, data architecture, and networking
• Cybersecurity, DevSecOps/CI-CD, and observability/SRE

Skills and Technologies

Enterprise ArchitectureAI GovernanceLLMs/AgentsCloud ArchitectureKubernetesData ArchitectureCybersecurityDevSecOps

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