Enterprise AI Architect

Qfyre TechLabs

Bengaluru

On-site

INR 3,000,000 - 6,000,000

Full time

2 days ago

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Job summary

Qfyre TechLabs seeks an experienced Enterprise AI Architect to lead end-to-end AI systems architecture, governance, and deployment strategy across models, data, and infrastructure. You will define standards, ensure security and compliance, and coordinate with cross-functional teams to deliver scalable AI platforms.

The role requires deep expertise in AI governance, LLMs/agents, cloud/hybrid architecture, and robust API/data integration capabilities to align technology with business goals.

Qualifications

12-15 years of enterprise architecture experience, with hands-on AI platforms and governance.
Experience in AI governance, LLMs, agents, and enterprise security controls.
Cloud and hybrid architecture experience, including Kubernetes and containerization.
APIs, data architecture, networking, and integration expertise.
Cybersecurity, DevSecOps/CI-CD, and observability/SRE knowledge.

Responsibilities

Define AI system architectures across models, data, APIs, infrastructure, and external services.
Establish architecture principles, reference architectures, and tech standards.
Define deployment patterns for AI systems in cloud, on-prem, and hybrid environments.
Develop reusable platform capabilities for AI products and governance.
Ensure security, compliance, auditability, and risk management across AI ecosystems.

Skills

Enterprise architecture

AI governance

Generative AI

LLMs/Agents

Cloud & hybrid

Kubernetes

APIs & Integration

Data architecture

Networking

Cybersecurity

DevSecOps/CI-CD

Observability/SRE

Tools

Kubernetes

Job description

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.

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

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

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.