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20+ years of experience required. Onsite role to Detroit, MI
Key Responsibilities:
Design and govern enterprise AI platform architecture, including LLM API gateways, GPU allocation, sandbox provisioning, model registries, and AI security controls.Architect multi-agent AI systems using LangGraph, LangChain, MCP, and enterprise-grade orchestration frameworks.Design and optimize RAG systems, vector search architectures, embedding strategies, and semantic search solutions.Lead AWS-native AI implementations using Bedrock, SageMaker, Amazon Q, EKS, Lambda, ECS, and related AWS services.Define Salesforce AI integration architecture with Agentforce, Einstein, Data Cloud, and Service Cloud.Establish MLOps and AgentOps practices for deployment, monitoring, evaluation, and governance of AI systems.Mentor engineering teams and lead enterprise architecture reviews and technical governance initiatives.
Required Technical Skills:
Expertise in LangGraph, LangChain, Bedrock, SageMaker, Amazon Q, MCP, and Agentic AI architecturesStrong AWS cloud architecture experience including EKS, Lambda, ECS Fargate, API Gateway, DynamoDB, OpenSearch, Athena, Redshift, etc.Advanced Python and FastAPI development experienceExperience with vector databases, embedding strategies, RAG pipelines, and graph databasesStrong CI/CD and Infrastructure as Code experience using Terraform, AWS CDK, GitHub Actions, and CodePipelineSalesforce AI integration experience with Agentforce, Einstein, Apex, Flow, and Platform EventsStrong knowledge of enterprise security, IAM, governance, compliance, and AI operational frameworks
Experience Requirements:
20+ years of software engineering experience5+ years focused on AI/ML systems3+ years of hands-on experience building production-grade LLM and Agentic AI applicationsProven experience leading enterprise-scale AI platform implementations
Preferred Certifications:
AWS Certified Solutions Architect ProfessionalAWS Certified Machine Learning SpecialtySalesforce AI certifications are a plus
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