Role Overview:

The AI Architect will provide senior, hands-on architectural leadership for enterprise AI initiatives. This role will help define scalable, secure, reusable, and governed AI architecture patterns across LLM applications, AI agents, RAG pipelines, MCP integrations, enterprise data platforms, cloud-native applications, and business workflows. You will work with Enterprise Architecture Office, divisional architects, engineering leaders, product teams, data teams, governance stakeholders, and delivery teams to move AI solutions from experimentation to production-ready enterprise capabilities.

This role requires cumulative technical knowledge across AI/ML, software engineering, Python, C#, SQL, cloud architecture, distributed systems, data platforms, APIs, security, DevOps, observability, and enterprise governance.

Key Responsibilities :

- Define and guide enterprise AI architecture patterns for AI-enabled platforms and workflows.
- Lead solution architecture for priority AI initiatives involving LLMs, AI agents, MCP servers, RAG pipelines, workflow automation, knowledge assistants, and intelligent governance tools.
- Drive the transition from isolated AI solutions to reusable, platform-based AI capabilities and shared architectural patterns.
- Apply reference architectures for AI agents, MCP server integration, RAG pipelines, vector databases, enterprise APIs, data access, security, observability, and governance.
- Provide hands-on architectural guidance across Python-based AI services, C#/.NET enterprise applications, SQL-based data platforms, APIs, microservices, and cloud-native systems.
- Design agent-based architectures involving tool-use orchestration, prompt design/versioning, workflow integration, context management, memory, human-in-the-loop controls, and auditability.
- Ensure AI solutions incorporate traceability, explainability, access control, entitlements, data lineage, observability, model lifecycle controls, and compliance requirements.
- Review architecture designs, assess technical trade-offs,



and recommend practical implementation approaches aligned with enterprise standards.
- Partner with engineering teams to define scalable integration patterns between AI systems and existing enterprise applications.
- Collaborate with Data Modelers and Data Engineers to ensure AI systems use governed, high-quality, well-structured, and AI-ready data.
- Support architecture governance by defining technical guardrails, design review criteria, and compliance checkpoints.
- Influence technology choices, vendor/platform evaluations, AI roadmap priorities, and implementation sequencing through clear technical and business trade-off analysis.
- Mentor internal architects, technical leads, and engineers through design reviews, workshops, and knowledge transfer sessions.

Required Qualifications :

- Bachelors or masters degree in Computer Science, Software Engineering, Artificial Intelligence, Data Engineering, or a related field.
- 7+ years of experience in software engineering, enterprise architecture, solution architecture, platform architecture, or senior technical leadership roles.
- Strong cumulative technical knowledge across AI/ML, LLMs, agentic systems, software engineering, cloud platforms, distributed systems, data architecture, APIs, DevOps, and security.
- Strong hands-on understanding of Python for AI engineering, automation, orchestration, and AI service development.
- Strong hands-on understanding of C#, .NET and enterprise backend application architecture.
- Robust SQL expertise and understanding of relational databases, data modeling, query optimization, and enterprise data access patterns.
- Proven experience designing enterprise-scale platforms or applications in complex, data-intensive environments.




- Experience architecting LLM-based applications, AI agents, RAG systems, workflow automation, and intelligent decision-support solutions.
- Experience with agentic AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, Google ADK, or similar frameworks.
- Strong understanding of MCP server architecture and how AI systems integrate with enterprise APIs, tools, repositories, and data platforms.
- Experience with cloud-native architecture on AWS, Azure, or Google Cloud.
- Strong knowledge of microservices, APIs, event-driven architecture, containers, CI/CD, infrastructure-as-code, observability, and production operations.
- Demonstrated ability to influence senior stakeholders, lead technical discussions, and guide complex delivery initiatives.

Preferred Qualifications :

- Experience in financial services, capital markets, ratings, risk analytics, commodities, fintech, or regulated enterprise environments.
- Experience with AWS services such as EC2, ECS, Lambda, RDS, API Gateway, SageMaker, Bedrock, CloudFormation, CDK, or comparable cloud services.
- Experience with vector databases such as OpenSearch, FAISS, Pinecone, or pgvector.
- Experience with AI governance, model lifecycle management, responsible AI, model risk controls, data privacy, and auditability.
- Experience supporting SaaS modernization, platform transformation, operating model transformation, or enterprise architecture governance.

Key Skills : Enterprise AI Architecture, Python, C#, SQL, LLMs, AI Agents, MCP Server, RAG, Microservices, Cloud Architecture, AWS, Azure, APIs, .NET, Data Architecture, Vector Databases, Security, Governance, Observability, Architecture Standards, Technical Leadership.

Expected Deliverables :

- AI reference architecture and solution architecture documents.
- Architecture decision records and design review recommendations.
- MCP, RAG, AI agent, data access, and security architecture patterns.
- Governance guardrails and architecture standards.
- Technical roadmap recommendations.Role & responsibilities

📌 AI Architect (Delhi)
🏢 Indexnine
📍 Delhi