AI Architect

AI Architect

Onsite in Auburn Hills, MI

Responsbilities

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

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