Technical AI Architect

ShineBask Technologies LLC is seeking a Technical AI Architect to lead the architecture and governance of AI platforms. The role involves leveraging advanced AI frameworks and machine learning techniques, as well as architecting scalable cloud-native systems on AWS.

Responsibilities:

• 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
• 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
• SageMaker (Studio, Pipelines, Model Registry, Inference), Bedrock, EKS, Lambda, ECS Fargate, API Gateway, Step Functions
• S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, Kendra
• Event Bridge, SNS/SQS, Kinesis, MSK
• CloudWatch, X-Ray, CloudTrail, AWS Config, GuardDuty, Macie, Security Hub
• IAM, KMS, PrivateLink, VPC design, and AWS Organizations governance
• 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
• 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)
• Vector store architecture 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
• 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