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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Work arrangement
Hybrid