AI Architect

Role: AI Architect

Location: Auburn Hills MI (Onsite)

Type: Contract

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

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 hyperparameter optimization cross-validation drift detection and model evaluation;
• end-to-end ML lifecycle on SageMaker spanning data preparation training deployment monitoring and retraining.

AWS Platform:

• 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

Salesforce and Enterprise SaaS:

• 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

Programming and Development:

• 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)

Data and Vector Systems:

• 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

Basic 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

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

Education:

• Bachelors or Masters degree in Computer Science AI/ML or a related technical field
• 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