AI Architect
On-siteNoida, Uttar Pradesh, India
Job Summary
Design enterprise-scale AI platforms and agentic systems using Python, C#, and cloud-native architectures on AWS, Azure, or Google Cloud. Architect LLM-based applications, RAG systems, and workflow automation while integrating MCP servers with enterprise APIs and data platforms. Lead solution and platform architecture for complex, data-intensive environments, ensuring security, observability, and production operations. This role requires 12+ years of experience in software engineering and technical leadership. Candidates must demonstrate hands-on expertise in distributed systems, SQL, and AI governance. NTT DATA offers hybrid work options subject to client requirements, with in-office attendance possible for meetings or events.
Required Qualifications
- 12+ 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
- Strong 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
Desired 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
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