Technical Architect - AI

Job Title: Technical Architect - AI Location: Auburn Hills, MI (Day 1 Onsite) Job Description We are seeking an experienced AI Technical Architect to lead the design, governance, and implementation of enterprise-scale AI platforms and agentic AI solutions. The ideal candidate will have deep expertise in AI/LLM architecture, AWS cloud services, MLOps, enterprise integrations, and security governance. Key Responsibilities Platform Architecture & Governance Design enterprise AI platform architecture including LLM API gateways, GPU/compute allocation, sandbox provisioning, model registry, and security automation. Define infrastructure standards, API gateway patterns, and reusable reference architectures for AI delivery teams and partner integrations. Establish governance controls for token metering, rate limiting, audit logging, DLP validation, SAST/DAST, dependency scanning, and model review processes within CI/CD pipelines. Ensure AI workloads align with NIST AI RMF, AWS Well-Architected Framework, ML Lens, and enterprise compliance standards. Agentic AI & LLM Engineering Architect multi-agent AI systems using LangGraph, LangChain, and MCP (Model Context Protocol). Design orchestration patterns including ReAct, Chain-of-Thought, Tree-of-Thoughts, and agent-to-agent collaboration. Build and optimize RAG architectures, semantic search frameworks, embedding pipelines, and enterprise knowledge retrieval systems. Establish MLOps and AgentOps practices for deployment, monitoring, evaluation, and continuous optimization of AI systems. AWS AI & Cloud Architecture Architect AI solutions using Amazon Bedrock, SageMaker, Amazon Q, Bedrock Agents, and Bedrock Knowledge Bases. Define scalable cloud-native patterns leveraging EKS, Lambda, ECS Fargate, API Gateway, EventBridge, SNS/SQS, Kinesis, S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, and Kendra. Develop infrastructure automation using AWS CDK, CloudFormation, and Terraform for secure multi-tenant sandbox environments. Implement observability, cost governance, and FinOps using CloudWatch, AWS Budgets, Cost Explorer, and chargeback models. Salesforce & SaaS AI Integration Design integrations with Salesforce Agentforce, Einstein, Data Cloud, and Service Cloud. Define integration patterns using Apex, Flow, Platform Events, and AWS-hosted AI services. Establish governance for enterprise SaaS AI licensing, usage optimization, and cross-platform identity management. Leadership & Collaboration Collaborate with AI leadership, security, compliance, procurement, and engineering teams to drive AI platform adoption. Create enterprise architecture documentation, decision records, and governance artifacts. Lead architecture reviews, technical due diligence, and mentor engineering teams across multiple delivery towers. Required Skills Strong experience in enterprise AI/LLM architecture and Agentic AI frameworks. Hands-on expertise with LangChain, LangGraph, MCP, RAG, and semantic search solutions. Deep AWS cloud architecture experience including Bedrock, SageMaker, EKS, Lambda, and serverless services. Experience with MLOps, AgentOps, CI/CD security automation, and observability. Knowledge of AI governance, security, and compliance frameworks. Experience integrating AI platforms with Salesforce ecosystems. Strong stakeholder management and enterprise architecture documentation skills.