Design and develop robust data pipelines using Databricks and AWS S3 while implementing the Lakehouse Medallion Architecture. Integrate and centralize data from multiple internal systems while performing independent unit testing and data validation.
Design, develop, and productionize Generative AI applications, intelligent agents, and automation workflows. Integrate foundation models with enterprise data, applications, and business processes while ensuring secure and scalable performance.
Design, develop, and operationalize Generative AI solutions using Amazon Bedrock across various platforms. Collaborate with cross-functional teams to integrate AI capabilities into production while ensuring scalability, security, and observability.
You will lead a high-output engineering pod to migrate AWS Lambda applications to Azure by designing robust agentic scaffolding and feedback loops. Your role involves balancing hands-on harness development with guiding the team to ensure agent-generated code remains coherent and reliable.
The FinOps Analyst will lead cloud product deployment projects and manage financial support processes including budget reconciliation and variance reporting. They are responsible for analyzing complex datasets to identify cost trends and developing automated chargeback workflows to improve financial efficiency.
Design and implement integration solutions using MuleSoft to connect EHR systems like Epic and Cerner. Manage healthcare data exchange using HL7 and FHIR standards while ensuring compliance with HIPAA security requirements.
The Senior Software Developer in Test will develop test automation within an Agile environment for backend services. They will collaborate closely with development teams to ensure software products and data tools scale with outstanding quality.
The role involves optimizing software delivery by building CI/CD pipelines and developing self-service tools for developers. You will also manage cloud infrastructure, containers, and VMs while ensuring system scalability, security, and reliability.
The Cloud AI Engineer will build multi-step Agentic AI workflows and provide technical client service. The role involves working with data pipelines, recommendation engines, and distributed machine learning systems.
The role involves architecting and maintaining a scalable, metadata-driven test automation framework for complex data pipelines and platforms. You will lead quality initiatives, enforce DataOps practices, and collaborate with cross-functional teams to ensure data reliability and observability.
Design, develop, and deploy production-ready Agentic AI and GenAI services while building scalable APIs and microservices. Collaborate with cross-functional teams to translate business requirements into robust AI solutions and maintain high-quality code standards.
Design and scale data architectures for machine learning lifecycles, including feature stores and model training pipelines. Build and maintain robust ETL/ELT pipelines while implementing automated testing to ensure data integrity.
