Technical Architect AI

Technical Architect - AI
Remote
FTE

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

Experience Requirements
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
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
Bachelors or Masters 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