- 15 or more years experience in software, platform, or cloud architecture

- Customer obsession, ownership and working backwards mindset

- At least 5+ years hands-on experience architecting and delivering cloud solutions that run in production for enterprise users, beyond PoC stage

- Deep understanding of AI/GenAI, large language models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI, tool and integration (incl.

Model Context

Protocol, MCP), and structured knowledge sources

- Applied experience with cloud AI platforms, minimum Azure AI Foundry and adjacent Azure services

- Experience with enterprise integration, APIs, event and data architectures, and identity and access management

- Understanding of security, data protection, AI governance, Responsible AI, and regulatory requirements (EU AI Act, GDPR)

- Experience with AI platform architecture, including scalability, observability, cost control, and lifecycle management

- Proven ability to translate requirements into cost effective target architectures, technical standards, and actionable architecture decisions

- Confident with SDD, CI/CD, Git, automated testing, code reviews, and contemporary DevOps practices

- Experience advising Product Owners, development teams, and stakeholders up to management level

- Familiarity with developer AI and agentic engineering (GitHub Copilot, coding agents, agent frameworks)

- Experience driving technology adoption across distributed, multi-site teams

- Hands-on mindset: validates architecture decisions through PoCs and prototypes

- Resilient, proactive, trustworthy, transparent, and team oriented.

Job Responsibilities

- Design and maintain the target architecture of the enterprise AI platform: LLM services, RAG pipelines,



agent runtimes, and tool integration

- Align AI solution architectures with Enterprise Architecture, and the engineering data strategy

- Define platform capabilities, architecture patterns, technical standards, and reusable services for AI solutions

- Translate use-case requirements into solution architectures and documented Architecture Decision Records (ADRs)

- Architect the integration of AI solutions with enterprise and engineering systems (M365, GitHub, Databricks, SAP, Salesforce) via APIs, events, MCP and A2A

- Define standards and guardrails for building and operating AI agents and GenAI solutions: evaluation, prompt and model versioning, tool permissions, human-in-the-loop

- Implement AI governance, security, and compliance requirements (EU AI Act, data protection, internal policies) together with Central IT Security

- Design identity and access patterns for AI solutions and agents (Entra ID, Keycloak, OAuth 2.0 / OIDC, on-behalf-of flows)

- Ensure scalability, observability, and lifecycle management of AI workloads across TEST / QA / PROD environments

- Create transparency on AI consumption and cost; define total cost of ownership (TCO) metrics and provide cost estimates for business cases

- Drive the technical roadmap and platform priorities together with the Product Owner

- Build and curate reusable AI assets (agents, prompts, skills, components) for the AI marketplace

- Validate architecture options through PoCs and prototypes before committing to implementation

- Guide Senior Developers through architecture and code reviews; collaborate with the SRE/DevOps team on deployment and incident analysis of AI workloads.

CTC Upto 70 LPA

Exp 15+ Yrs

Location: Gurgaon

📌 AI Lead Architect (Gurugram)
🏢 Talent Integrators
📍 Gurugram