Overview

AI/ML Enterprise Architect

Responsibilities

• Define enterprise AI/ML architecture, technology strategy, and reference architectures aligned with business and technology objectives.
• Design scalable, secure, and highly available GenAI, AI/ML, and agentic AI solutions for enterprise use cases.
• Architect enterprise integrations across applications, APIs, data platforms, AI services, and third-party systems.
• Design and implement A2A and MCP-based architectures for AI agents and enterprise AI ecosystems.
• Evaluate and recommend appropriate AI models, agent frameworks, cloud services, APIs, vector databases, and integration technologies.
• Leverage Microsoft Azure AI services and other cloud-native capabilities to build enterprise-grade AI solutions.
• Define architecture patterns for chatbots, virtual assistants, AI agents, RAG-based applications, and enterprise AI platforms.
• Establish architectural standards covering security, scalability, performance, reliability, observability, governance, and compliance.
• Collaborate with engineering, product, security, data, and business teams to translate requirements into scalable technical solutions.
• Conduct architecture reviews and provide technical direction to development and engineering teams.
• Assess emerging AI technologies and frameworks and recommend their adoption where they provide business value.
• Identify technical risks, architecture gaps, and integration challenges and define appropriate mitigation strategies.
• Create and maintain solution architecture documents, technical specifications, architecture diagrams, and technology roadmaps.
• Ensure AI solutions follow enterprise security and responsible AI principles.

Qualifications

• Location: Delhi/Kolkata
• Employment Type: Full-Time
• Job Summary
• We are looking for an experienced AI/ML Enterprise Architect to define and drive scalable enterprise architecture and technology strategy, with strong expertise in AI/ML, Generative AI, cloud architecture, enterprise integrations, APIs, and security.
• The ideal candidate will have a strong track record of designing enterprise-grade AI solutions, integrating AI capabilities with existing business applications, and making strategic architectural decisions across cloud and hybrid environments. The candidate should also have hands-on exposure to A2A (Agent-to-Agent) communication and MCP (Model Context Protocol) and experience building or architecting AI-powered assistants, chatbots, and agentic solutions.
• 8–12 years of overall experience in software architecture, solution architecture, enterprise architecture, or related technology roles.
• Strong experience in AI/ML and Generative AI architecture and enterprise AI solution design.
• Strong understanding of enterprise integration architecture, APIs, microservices, event-driven architecture, and distributed systems.
• Hands-on experience with A2A (Agent-to-Agent) and MCP (Model Context Protocol).
• Strong knowledge of Microsoft Azure AI services, Azure cloud architecture, and cloud-native technologies.
• Experience with AI/agent frameworks, LLM-based applications, RAG, tool calling, and agentic workflows.
• Experience designing or architecting chatbots, virtual assistants, conversational AI, or enterprise AI platforms.
• Strong understanding of API management, authentication/authorization, identity, security, and enterprise integration patterns.
• Experience with cloud platforms, preferably Microsoft Azure.
• Strong understanding of AI/ML lifecycle, model integration, deployment, monitoring, and governance.
• Strong solution design and architectural decision-making capabilities.
• Excellent understanding of scalability, high availability, performance, security, and resilience principles.
• Strong communication and stakeholder-management skills, with the ability to explain complex technical concepts to both technical and business stakeholders.
• Experience with Azure OpenAI / Microsoft Foundry, Azure AI Search, Azure AI services, or equivalent AI platforms.
• Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar agent frameworks.
• Knowledge of RAG architectures, vector databases, embeddings, prompt engineering, and LLM orchestration.
• Experience with Docker, Kubernetes, CI/CD, and DevSecOps.
• Exposure to AI governance, responsible AI, data privacy, and enterprise security frameworks.
• Experience working with large-scale enterprise transformation or modernization initiatives.