AI/ML Enterprise Architect

TuTeck Technologies; Delhi; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

TuTeck Technologies seeks an experienced AI/ML Enterprise Architect to define scalable enterprise architecture and technology strategy. You will lead reference architectures for GenAI, AI/ML, and agentic AI solutions across cloud and hybrid environments.

The role requires hands-on experience with A2A and MCP, enterprise integrations, APIs, and security, guiding architectural decisions and platform roadmaps for large-scale AI initiatives.

Qualifications

• 8–12 years of experience in software or enterprise architecture roles.
• Strong experience in AI/ML and Generative AI architecture.
• Experience with enterprise integration, APIs, microservices, and event-driven architectures.
• Hands-on exposure to A2A and MCP.
• Knowledge of Microsoft Azure cloud and AI services.
• Experience designing AI-powered assistants, chatbots, and agentic solutions.
• Ability to translate requirements into scalable technical solutions.

Responsibilities

• Define enterprise AI/ML architecture and reference architectures aligned to business goals.
• Design scalable, secure GenAI and agentic solutions for enterprise use cases.
• Architect integrations across applications, APIs, data platforms, AI services, and third-party systems.
• Design and implement A2A and MCP-based architectures for AI agents and ecosystems.
• Evaluate AI models, agent frameworks, cloud services, APIs, and vector databases.
• Leverage Azure AI services to build enterprise-grade AI solutions.
• Define patterns for chatbots, virtual assistants, and RAG-based apps.
• Establish standards covering security, scalability, performance, observability, governance.

Skills

AI/ML architecture

Generative AI

Azure AI services

API management

Cloud architecture

Enterprise integration

Security

A2A MCP

Tools

Docker

Kubernetes

LangChain

LangGraph

Semantic Kernel

AutoGen

Job description

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.