Technical Architect

POSITION OVERVIEW : Technical Architect - AI Description
POSITION OVERVIEW : Technical Architect - AI POSITION GENERAL DUTIES AND TASKS : Job Requirements
'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'
Technical Experience
'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 l