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

Required Skills

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

Ideal Years Of Experience

• 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

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

Additional Requirements

• 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