AI Solution Architect​/FDE; AWS and Azure

Req : 391925

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization,

We are currently seeking a AI Solution Architect / FDE (AWS and Azure) - to join our team in Toronto, Ontario (CA-ON), Canada (CA).

10+ years software engineering / technology experience | 3+ years hands-on AI/ML

AI/ML Architecture | Generative AI | RAG | Multi-Agent Systems | Production AI

Financial Services / Regulated Industries preferred

We are seeking an experienced AI Lead / AI Solutions Architect to lead the design, development, and implementation of enterprise AI solutions across Financial Services
.

This role combines AI/ML engineering, solution architecture, technical leadership, and strategic problem-solving
. The ideal candidate will be able to take AI concepts from research and experimentation through architecture, development, deployment, and production—while translating complex technology into measurable business value.

You will work closely with data scientists, ML engineers, software developers, product teams, and business stakeholders to deliver scalable AI solutions across areas such as document intelligence, intelligent automation, decisioning, and Generative AI
.

• Architect end-to-end AI/ML solutions from concept and experimentation through production deployment.
• Design scalable architectures for AI-powered applications, APIs, data pipelines, and intelligent workflows.
• Evaluate emerging AI technologies and determine how they can be applied to real-world business challenges.
• Lead technical decisions involving Generative AI, RAG, LLMs, multi-agent systems, NLP, and transformer-based models
  .
• Establish standards and best practices for building secure, reliable, scalable, and maintainable AI solutions.
• Balance technical innovation with business requirements, scalability, performance, security, and cost.

• Architect end-to-end AI/ML solutions from concept and experimentation through production deployment.
• Design scalable architectures for AI-powered applications, APIs, data pipelines, and intelligent workflows.
• Evaluate emerging AI technologies and determine how they can be applied to real-world business challenges.
• Lead technical decisions involving Generative AI, RAG, LLMs, multi-agent systems, NLP, and transformer-based models
  .
• Establish standards and best practices for building secure, reliable, scalable, and maintainable AI solutions.
• Balance technical innovation with business requirements, scalability, performance, security, and cost.

• Lead the development and implementation of production-grade AI/ML solutions.
• Develop and evaluate machine learning models and AI applications using modern frameworks and techniques.
• Design and optimize RAG pipelines, LLM applications, prompt strategies, and multi-agent architectures
  .
• Work with document intelligence, PDF processing, text extraction, NLP, and automated decisioning use cases.
• Develop APIs and services that integrate AI capabilities into enterprise applications.
• Establish model evaluation, monitoring, performance measurement, and continuous improvement processes.

• Lead complex AI initiatives from discovery and prototyping through production.
• Own solution architecture, technical design, implementation strategy, and delivery planning.
• Coordinate teams across AI/ML engineering, software development, data science, infrastructure, and business functions.
• Translate business requirements into technical solutions, architecture, and implementation roadmaps.
• Identify technical risks, dependencies, and opportunities early in the development lifecycle.
• Ensure successful transition from experimentation and proof-of-concept to scalable production solutions.

• Design and maintain deployment pipelines for AI/ML applications and models.
• Establish practices for model deployment, monitoring, versioning, testing, and lifecycle management.
• Support integration of AI solutions with enterprise systems, APIs, databases, and data platforms.
• Promote automation and repeatable engineering practices across the AI development lifecycle.
• Help establish standards for production readiness, observability, reliability, and governance.

• Mentor engineers and technical team members in AI/ML engineering and software development…