Staff Cloud and AI Integration Engineer

Design, develop, and maintain scalable microservices‑based applications on a major cloud provider.

Develop backend services with clean, maintainable, testable code.

Ensure availability, resiliency, scalability, performance, and observability across services.

Contribute to system architecture including service decomposition, data flows, and integration patterns.

Apply distributed systems best practices including fault tolerance, idempotency, caching, and asynchronous or event‑driven patterns.

Promote cloud‑first and API‑first architectural principles.

Participate in design reviews and provide technical leadership on architecture decisions.

Implement Infrastructure as Code (IaC) using tools such as Terraform, Pulumi, or native cloud frameworks.

Develop and maintain CI/CD pipelines for automated build, test, security scanning, and deployment.

Use Docker and Kubernetes for containerization and orchestration.

Build and deploy services using compute, storage, networking, and data services from any major cloud provider.

Implement context providers, adapters, and orchestration layers that enable reliable interactions between applications and AI models.

Develop pipelines for prompt engineering, context retrieval, tool invocation, rate limiting, and response orchestration.

Integrate with hosted AI platforms to operationalize AI‑driven features.

Implement guardrails, validation, monitoring, and safety measures to ensure responsible AI usage

Design and build MCP (Model Context Protocol) servers and supporting components to integrate enterprise systems, data sources, and workflows with LLMs.

Collaborate effectively with frontend engineers and understand how backend services integrate with TypeScript‑based UI components.

Work with Data Science, Applied AI, Platform, and Product teams to deliver end‑to‑end features.

Ensure secure and compliant handling of sensitive healthcare data when applicable.

Translate business requirements into scalable technical implementations.

Participate in code reviews, quality practices, and continuous improvement.

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

6+ years of experience building cloud‑native applications.

Hands‑on experience with:

• Cloud platforms such as AWS, Azure, or GCP

• Microservices architecture

• Docker and Kubernetes

• CI/CD pipelines

• Infrastructure as Code using Terraform, Pulumi, or native cloud frameworks

Strong understanding of software architecture and distributed systems.

Working knowledge of (6+ years experience):

• LLMs, RAG, and Agentic AI concepts

• AI‑based workflow integration including prompting, grounding, and orchestration

Master’s degree in Data Science fields

Experience integrating Generative AI features into production systems.

Experience in healthcare or medical technology domains.

Understanding of DICOM standards or imaging workflows.

Building server components or integration layers, including protocol‑based services such as MCP servers

Strong architectural thinking and systems problem‑solving.

Ability to design and build scalable cloud‑native systems with operational excellence.

Curiosity and adaptability with emerging AI technologies and patterns.

Excellent debugging and troubleshooting skills across distributed systems.

Effective communication and collaboration across cross‑functional teams.