AI Architect

JOB SUMMARY

The Technical Architect - AI will design and implement enterprise AI platform architecture, focusing on LLM API gateway, compute allocation, sandbox provisioning, model registry, and security automation. This role involves defining infrastructure standards, API gateway patterns, and reference architectures for AI delivery towers and partner integrations. The architect will establish guardrails for various AI governance aspects within CI/CD pipelines and review the security posture of AI workloads against recognized security frameworks. Key responsibilities extend to architecting multi-agent systems, optimizing RAG systems, and establishing MLOps and AgentOps practices. The role also requires architecting AWS-native solutions, integrating with Salesforce AI capabilities, managing SaaS AI licenses, and leading cross-system identity and data exchange patterns. Collaboration with leadership, delivery teams, and stakeholders is essential to ensure platform adoption and adherence to operating standards. Key Responsibilities

• Design the enterprise AI platform architecture, including 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.
• Review security posture across 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-to-agent coordination, including ReAct, Chain-of-Thought, and Tree-of-Thoughts.
• Design and optimize Retrieval-Augmented Generation (RAG) systems, embedding strategies, and semantic search.
• Establish MLOps and AgentOps practices for deployment, evaluation, observability, and continuous improvement of agents and models.
• 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, 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 leadership, delivery tower leads, security, compliance, procurement, and program management to ensure platform adoption and operating standards.
• Produce enterprise-grade architecture artifacts, decision records, and operating model documentation.
• Mentor engineers across delivery towers and partner teams, and lead architecture reviews and technical due diligence. Required Qualifications
• 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.
• Experience Architecting multi-agent systems for complex workflow orchestration, planning, and tool use.
• Experience defining patterns for ReAct, Chain-of-Thought, Tree-of-Thoughts, and agent-to-agent coordination.
• Experience designing and optimizing Retrieval-Augmented Generation (RAG) systems, embedding strategies, and semantic search.
• Experience establishing MLOps and AgentOps practices for deployment, evaluation, observability, and continuous improvement.
• Experience architecting solutions on Amazon Bedrock, Amazon SageMaker, Amazon Q, Bedrock Agents, and Bedrock Knowledge Bases.
• Experience defining infrastructure patterns using Amazon

EKS, AWS

Lambda, ECS Fargate, API Gateway, EventBridge, SNS/SQS, Kinesis, S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, and Kendra.

• Experience establishing CloudFormation and AWS CDK templates and Terraform modules for isolated VPC sandboxes.
• Experience implementing observability and FinOps using CloudWatch, AWS Cost Explorer, AWS Budgets, and chargeback reporting.
• Experience defining integration architecture with Salesforce Agentforce, Einstein, Data Cloud, and Service Cloud, including Apex, Flow, and Platform Event integration patterns.
• Experience establishing governance over enterprise SaaS AI licenses.
• Experience architecting cross-system identity, authorization, and data exchange patterns.
• Experience partnering with leadership, delivery teams, security, compliance, and program management.
• Experience producing enterprise-grade architecture artifacts, decision records, and operating model documentation.
• Experience mentoring engineers and leading architecture reviews and technical due diligence.
• Experience with Classical ML (regression, tree-based ensembles, gradient boosting, clustering) and deep learning.