AI Architect

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