AI Technical Architect - Onsite

Req ID: 371562 

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, apply now.

 

We are currently seeking a AI Technical Architect - Onsite to join our team in Auburn Hills, Michigan (US-MI), United States (US).

 

Job Requirements                

Platform Architecture and Governance

•    Design the enterprise AI platform architecture spanning the LLM API gateway, GPU and compute allocation pools, sandbox provisioning, model registry, and security gate automation
•    Define infrastructure standards, API gateway patterns, and reference architectures consumed by all AI delivery towers and partner integrations
•    Establish guardrails for token metering, rate limiting, audit logging, DLP validation, SAST, DAST, dependency scanning, and model card review embedded in CI/CD
•    Review security posture across all AI workloads with mapping to NIST AI RMF, AWS Well-Architected (including the Machine Learning Lens), and applicable enterprise compliance baselines

 

Agentic AI and LLM Engineering

•    Architect multi-agent systems using LangGraph, LangChain, and Model Context Protocol (MCP) for complex workflow orchestration, planning, and tool use
•    Define patterns for ReAct, Chain-of-Thought, Tree-of-Thoughts, and agent-to-agent coordination across enterprise and customer-facing use cases
•    Design and optimize Retrieval-Augmented Generation (RAG) systems, embedding strategies, and semantic search across structured and unstructured enterprise data
•    Establish MLOps and AgentOps practices for deployment, evaluation, observability, and continuous improvement of agents and models in production

 

AWS-Native Implementation

•    Architect solutions on Amazon Bedrock, Amazon SageMaker, Amazon Q, Bedrock Agents, and Bedrock Knowledge Bases
•    Define infrastructure patterns using Amazon EKS, AWS Lambda, ECS Fargate, API Gateway, EventBridge, SNS/SQS, Kinesis, S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, and Kendra
•    Establish CloudFormation and AWS CDK templates and Terraform modules for isolated VPC sandboxes provisioned per project and per third-party partner
•    Implement observability and FinOps using CloudWatch, AWS Cost Explorer, AWS Budgets, and chargeback reporting by team, project, and model

 

Salesforce and SaaS AI Integration

•    Define integration architecture with Salesforce Agentforce, Einstein, Data Cloud, and Service Cloud, including Apex, Flow, and Platform Event integration patterns with AWS-hosted agents and APIs
•    Establish governance over enterprise SaaS AI licenses, including usage tracking, renewal governance, and redundancy elimination across business units
•    Architect cross-system identity, authorization, and data exchange patterns spanning Salesforce, AWS, and partner endpoints

 

Stakeholder and Delivery Leadership

•    Partner with AIDO leadership, delivery tower leads, security, compliance, procurement, and program management to ensure platform adoption and consistent operating standards
•    Produce enterprise-grade architecture artifacts, decision records, and operating model documentation suitable 
•    Mentor engineers across delivery towers and partner teams; lead architecture reviews and technical due diligence on partner-built systems"            
 

Technical Experience  

             
Core AI Frameworks
•    Expert proficiency with LangGraph, LangChain, and agent orchestration frameworks
•    Deep experience with Amazon Bedrock, SageMaker, and Amazon Q, including Bedrock Agents and Knowledge Bases
•    Hands-on experience with Model Context Protocol (MCP), function calling, tool use, and structured output patterns
•    Strong command of prompt engineering, evaluation harnesses, fine-tuning, and model optimization
•    Working knowledge of transformer architectures, attention mechanisms, and multi-modal systems

 

Machine Learning

 

•    Classical ML (regression, tree-based ensembles, gradient boosting, clustering) and deep learning (CNNs, RNNs, transformers) across supervised, unsupervised, and reinforcement paradigms; feature engineering, hyperparame