Technical Architect - AI | 20+ year

We are seeking an experienced Technical Architect - AI to lead enterprise AI platform architecture, agentic AI engineering, AWS-native AI implementations, and enterprise SaaS integrations. The ideal candidate will drive the design, governance, scalability, and security of enterprise AI systems while collaborating with cross-functional stakeholders and delivery teams. Key Responsibilities

• Design enterprise AI platform architecture including LLM API gateways, GPU/compute allocation pools, sandbox provisioning, model registry, and security automation
• Define infrastructure standards, API gateway patterns, and reference architectures for AI delivery teams and partner integrations
• Establish governance guardrails for token metering, rate limiting, audit logging, DLP validation, SAST/DAST, dependency scanning, and model card reviews within CI/CD pipelines
• Review AI workload security posture aligned with NIST AI RMF, AWS Well-Architected Framework (including ML Lens), and enterprise compliance standards

Agentic AI & LLM Engineering

• Architect multi-agent systems using LangGraph, LangChain, and Model Context Protocol (MCP)
• Define orchestration patterns including ReAct, Chain-of-Thought, Tree-of-Thoughts, and agent-to-agent coordination
• Design and optimize RAG systems, embedding strategies, and semantic search across enterprise data sources
• Establish MLOps and AgentOps practices for deployment, observability, evaluation, and continuous improvement of AI agents and models

AWS-Native AI Implementation

• Architect solutions using Amazon Bedrock, SageMaker, Amazon Q, Bedrock Agents, and Knowledge Bases
• Define infrastructure patterns leveraging Amazon EKS, Lambda, ECS Fargate, API Gateway, EventBridge, SNS/SQS, Kinesis, S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, and Kendra
• Develop CloudFormation templates, AWS CDK implementations, and Terraform modules for isolated VPC sandbox provisioning
• Implement observability and FinOps using CloudWatch, AWS Cost Explorer, AWS Budgets, and chargeback reporting
• Define integration architecture with Salesforce Agentforce, Einstein, Data Cloud, and Service Cloud
• Establish governance for enterprise SaaS AI licensing, usage tracking, and optimization
• Architect secure cross-system identity, authorization, and data exchange patterns across Salesforce, AWS, and partner systems

Stakeholder & Delivery Leadership

• Partner with AI leadership, delivery teams, security, compliance, procurement, and program management to drive platform adoption and operating standards
• Produce enterprise architecture documentation, decision records, and operating model artifacts
• Mentor engineering teams, conduct architecture reviews, and lead technical due diligence for partner-built systems

• Expert-level proficiency with LangGraph, LangChain, and agent orchestration frameworks
• Deep experience with Amazon Bedrock, SageMaker, Amazon Q, Bedrock Agents, and Knowledge Bases
• Hands-on expertise with MCP, function calling, tool usage, and structured output patterns
• Strong knowledge of prompt engineering, evaluation frameworks, fine-tuning, and model optimization
• Working knowledge of transformer architectures, attention mechanisms, and multimodal AI systems
• Experience with classical ML techniques including regression, clustering, tree-based models, and gradient boosting
• Deep learning expertise across CNNs, RNNs, transformers, supervised, unsupervised, and reinforcement learning paradigms
• Strong experience with feature engineering, hyperparameter tuning, drift detection, cross-validation, and model evaluation
• End-to-end ML lifecycle management using SageMaker including training, deployment, monitoring, and retraining

AWS Platform Expertise

• SageMaker, Bedrock, EKS, Lambda, ECS Fargate, API Gateway, Step Functions
• S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, and Kendra
• EventBridge, SNS/SQS, Kinesis, and MSK
• CloudWatch, X-Ray, CloudTrail, AWS Config, GuardDuty, Macie, and Security Hub
• IAM, KMS, PrivateLink, VPC architecture, and AWS Organizations governance
• Salesforce Agentforce, Einstein, Data Cloud, Service Cloud, and Sales Cloud integration patterns
• Apex, Flow, Platform Events, and REST/Bulk API integrations with external AI services
• Familiarity with SSO, OAuth, SCIM provisioning, and enterprise identity providers

Programming & Development

• Advanced Python expertise with FastAPI for scalable async APIs
• Java integration experience with enterprise Back End systems
• Strong CI/CD knowledge using AWS CodePipeline, CodeBuild, GitHub Actions, Terraform, and AWS CDK
• Experience with Docker and Kubernetes (EKS)

Data & Vector Systems

• Experience with vector databases including OpenSearch, Pinecone, Weaviate, Chroma, and Bedrock Knowledge Bases
• Knowledge of embedding strategies, hybrid search, and reranking approaches
• Experience with graph databases such as Amazon Neptune or Neo4j
• Expertise in data ingestion, masking, synthetic data generation, and DLP validation pipelines

Experience Requirements

• 20+ years of software engineering experience with 5+ years focused on AI/ML systems
• 3+ years of hands-on experience building and deploying production LLM and agentic AI applications
• Proven success leading enterprise-scale AI platform implementations with measurable business impact
• Strong experience architecting scalable AWS cloud-native systems in enterprise or regulated environments
• Demonstrated leadership experience mentoring teams and engaging executive stakeholders

Education & Certifications

• Bachelor's or Master's degree in Computer Science, AI/ML, or related field
• AWS Certified Solutions Architect - Professional or AWS Certified Machine Learning Specialty preferred
• Salesforce AI Associate, AI Specialist, or Application Architect certifications are a plus