AI Architect

JOB SUMMARY

This role involves designing and implementing an enterprise AI platform, focusing on LLM integration, agentic AI systems, and AWS-native solutions. Responsibilities include defining platform architecture, establishing governance and security guardrails, architecting multi-agent systems, optimizing RAG pipelines, and integrating with Salesforce and other SaaS AI products. The position requires collaboration with various stakeholders, mentoring engineering teams, and producing comprehensive architecture documentation. The role is based onsite in Auburn Hills, MI. Key Responsibilities Design the enterprise AI platform architecture encompassing 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 for 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 within CI/CD pipelines. Review the security posture of AI workloads against

NIST AI RMF, AWS

Well-Architected (including Machine Learning Lens), and enterprise compliance baselines. Architect multi-agent systems using LangGraph, LangChain, and Model Context Protocol (MCP) for workflow orchestration, planning, and tool use. Define patterns for agent coordination, ReAct, Chain-of-Thought, and Tree-of-Thoughts for enterprise and customer-facing use cases. Design and optimize Retrieval-Augmented Generation (RAG) systems, embedding strategies, and semantic search across enterprise data. Establish MLOps and AgentOps practices for deployment, evaluation, observability, and continuous improvement of agents and models. Architect AWS-native 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, AWS CDK templates, and Terraform modules for isolated VPC sandboxes. Implement observability and FinOps using CloudWatch, AWS Cost Explorer, AWS Budgets, and chargeback reporting. Define integration architecture with Salesforce Agentforce, Einstein, Data Cloud, and Service Cloud. Establish governance over enterprise SaaS AI licenses, including usage tracking, renewal governance, and redundancy elimination. 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. Mentor engineers across delivery towers and partner teams; lead architecture reviews and technical due diligence on partner-built systems. Required Qualifications 20+ years in software engineering with 5+ years focused on AI/ML systems. 3+ years hands-on experience architecting and shipping production LLM and agentic AI applications. 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. 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. Experience with end-to-end ML lifecycle on SageMaker spanning data preparation, training, deployment, monitoring, and retraining. Familiarity with SageMaker (Studio, Pipelines, Model Registry, Inference), Bedrock, EKS, Lambda, ECS Fargate, API Gateway, Step Functions, S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, Kendra, EventBridge, SNS/SQS, Kinesis, MSK, CloudWatch, X-Ray, CloudTrail, AWS Config, GuardDuty, Macie, Security Hub, IAM, KMS, PrivateLink, VPC design, and AWS Organizations governance. Familiarity with Salesforce Agentforce, Einstein, Data Cloud, Service Cloud, and Sales Cloud integration patterns, Apex, Flow, Platform Events, and REST/Bulk API integration with external AI services. Familiarity with enterprise identity providers, SSO, OAuth, and SCIM provisioning across SaaS estates. Advanced Python with deep FastAPI experience for scalable, async API development. Java proficiency sufficient to integrate with existing enterprise backend services. Strong CI/CD background using AWS CodePipeline, CodeBuild, GitHub Actions, and Infrastructure as Code via Terraform and AWS CDK. Containerization with Docker and orchestration with Kubernetes (EKS). Vector store architectures using OpenSearch, Bedrock Knowledge Bases, Pinecone, Weaviate, or Chroma. Embedding model selection, hybrid search, and reranking strategies. Graph database experience (Amazon Neptune, Neo4j) for knowledge representation. Data ingestion, masking, synthetic data generation, and DLP validation pipelines. Preferred Qualifications Demonstrated success leading enterprise-scale AI platform builds with measurable business outcomes. Track record architecting scalable cloud-native systems on AWS in regulated or large-enterprise environments. Experience leading technical teams, mentoring engineers, and engaging executive stakeholders. Certifications AWS Certified Solutions Architect Professional or AWS Certified Machine Learning Specialty preferred. Salesforce Certified AI Associate, AI Specialist, or Application Architect credentials is a plus.

Education:

Doctoral Degree Certification:

AWS Certified Solutions Architect Professional , AWS Certified Machine Learning Specialty , Salesforce Certified AI Associate , AI Specialist , Application Architect credentials