AI Engineer

Job Summary You will develop and deploy AI/ML solutions focusing on Amazon Bedrock knowledge bases, skill marketplaces, and Microsoft Exchange integration, while supporting backend development and cloud infrastructure to enhance system observability, security, and cost management. Responsibilities Design and implement document ingestion pipelines, vector indexing, and retrieval-augmented generation (RAG) tuning for Amazon Bedrock knowledge bases to optimize AI/ML model performance Develop and validate skill packaging and integrate AgentCore gateway with version control to enhance skills marketplace functionality Integrate Microsoft Exchange connector using Graph API and OAuth, managing draft-review-send workflows with human-in-the-loop patterns to improve communication processes Deploy AI/ML applications to ECS and AgentCore runtime environments ensuring reliable operation Aggregate APIs and shape responses in the backend-for-frontend (BFF) layer to streamline data delivery Implement authentication and session management using federated Active Directory and OAuth token exchange to secure user access Manage access control at agent, skill, and knowledge base levels using IAM policies, resource-level permissions, and sharing workflows to enforce security Monitor agent and large language model (LLM) performance using CloudWatch dashboards and Bedrock model invocation metrics to maintain system health Track costs and allocate billing through resource tagging and Cost Explorer reporting by user and department to optimize budget management Maintain audit logs for permission changes, agent/skill access, and knowledge base activity to ensure compliance and traceability Provision cloud infrastructure using Infrastructure as Code tools such as CloudFormation and CDK to automate environment setup Manage multi-environment deployments and go-live processes using CI/CD pipelines, runbooks, and scaling policies to ensure smooth releases Preferred competencies and qualifications Experience in backend or application development focusing on API aggregation and authentication mechanisms Familiarity with DevOps practices including observability, cost tracking, audit logging, and infrastructure automation