AI Architect

AI Architect

Onsite in Auburn Hills, MI

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

Architect multi-agent systems using Lang Graph, Lang Chain, 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 Agent Ops practices for deployment, evaluation, observability, and continuous improvement of agents and models in production

Architect solutions on Amazon Bedrock, Amazon Sage Maker, Amazon Q, Bedrock Agents, and Bedrock Knowledge Bases

Define infrastructure patterns using Amazon EKS, AWS Lambda, ECS Fargate, API Gateway, Event Bridge, SNS/SQS, Kinesis, S3, DynamoDB, Aurora, Redshift, Athena, Open Search, and Kendra

Establish Cloud Formation and AWS CDK templates and Terraform modules for isolated VPC sandboxes provisioned per project and per third-party partner

Implement observability and Fin Ops using Cloud Watch, AWS Cost Explorer, AWS Budgets, and chargeback reporting by team, project, and model

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

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

Expert proficiency with Lang Graph, Lang Chain, and agent orchestration frameworks

Deep experience with Amazon Bedrock, Sage Maker, 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

Classical ML (regression, tree-based ensembles, gradient boosting, clustering) and deep learning (CNNs, RNNs, transformers) across supervised, unsupervised, and reinforcement paradigms; feature engineering, hyperparameter optimization, cross-validation, drift detection, and model evaluation;

end-to-end ML lifecycle on Sage Maker spanning data preparation, training, deployment, monitoring, and retraining.