Remote AI Architect: Agentic Systems Lead

NTT America, Inc.; Plano (TX); Remote; USD 123,000 - 284,000; Full time

Job summary

NTT DATA in Plano, Texas seeks an AI Architect - Senior AI Engineer to design and build agentic AI systems across healthcare, finance, and retail domains. You will combine GenAI, ML, and systems engineering to create AI agents that interact with machines, data streams, and enterprise systems.

You will deploy and monitor AI solutions in Azure, work with subject-matter experts, and translate medical workflows into agent logic, with a focus on production-grade, auditable architectures and

Qualifications

• 8+ years of Python-based AI systems experience.
• 8+ years of GenAI (LLMs, RAG, embeddings, prompt engineering).
• 8+ years of traditional ML (classification, regression, NLP, time-series).
• 5+ years of full-stack API design plus UI integration.
• 5+ years of Azure cloud experience (Azure ML, Azure OpenAI, Functions, AKS).
• 5+ years in regulated or compliance-heavy domains.
• Experience working with SMEs and production AI systems.

Responsibilities

• Design and implement agent-based AI workflows across multiple domains.
• Build agentic AI architectures for healthcare, finance, and manufacturing.
• Develop multi-agent AI systems including RAG and LLM-powered components.
• Integrate AI with EHRs, data lakes, and enterprise systems.
• Deploy, monitor, and optimize AI systems in Azure.
• Explain AI decisions to non-technical stakeholders.

Skills

Python-based AI systems

GenAI (LLMs, RAG, embeddings, prompt工程

Education

Bachelor's in Computer Science

Tools

Azure ML

Azure OpenAI

Functions

AKS

Job description

NTT DATA in Plano, Texas seeks an AI Architect - Senior AI Engineer to design and build agentic AI systems across healthcare, finance, and retail domains. You will combine GenAI, ML, and systems engineering to create AI agents that interact with machines, data streams, and enterprise systems.

You will deploy and monitor AI solutions in Azure, work with subject-matter experts, and translate medical workflows into agent logic, with a focus on production-grade, auditable architectures and