AI/ML Enterprise Architect
Experience: 8–12 Years
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.
Key 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.
Required Skills & Experience
- 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.
Good to Have
- 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.
