Remote AI Solutions Architect - Generative AI & Production

NTT DATA, Inc.; Sacramento (CA); Remote; USD 164,000 - 252,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401k with company match

Paid time off

Employee assistance program

Life and AD&D insurance

Disability insurance

Job summary

NTT DATA, Inc. is seeking an AI Lead / AI Solutions Architect to design and deploy enterprise AI solutions in Financial Services. You will lead AI/ML engineering, solution architecture, and technical delivery across teams, turning research into production-grade systems.

You will work with data scientists, software engineers, and business stakeholders to build document intelligence, automation, and decisioning capabilities, while upholding governance, security, and scalable performance.

Qualifications

• 10+ years of professional software engineering, technology, or relevant research experience.
• 3+ years of hands-on AI/ML engineering experience in production environments.
• Expert-level Python development skills.
• Strong experience designing and implementing end-to-end AI/ML solutions.

Responsibilities

• Architect end-to-end AI/ML solutions from concept through production deployment.
• Lead technical decisions involving Generative AI, RAG, LLMs, multi-agent systems, NLP, and transformer-based models.
• Design scalable architectures for AI-powered applications, APIs, data pipelines, and intelligent workflows.
• Establish standards for production readiness, observability, and governance.

Skills

AI/ML engineering

Python development

LLMs & Generative AI

Solution architecture

Team leadership

Tools

PyTorch

TensorFlow

Keras

Hugging Face Transformers

LangChain

FastAPI

Job description

NTT DATA, Inc. is seeking an AI Lead / AI Solutions Architect to design and deploy enterprise AI solutions in Financial Services. You will lead AI/ML engineering, solution architecture, and technical delivery across teams, turning research into production-grade systems.

You will work with data scientists, software engineers, and business stakeholders to build document intelligence, automation, and decisioning capabilities, while upholding governance, security, and scalable performance.