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:
Bachelor's or Master's 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