Note: The job is a remote job and is open to candidates in USA. Medecision is seeking a Senior Full-Stack Software Engineer for their Clinical Intelligence team to develop market-transforming solutions in healthcare. The role involves designing and building microservices and UI components for a care intelligence platform, utilizing AI tools to enhance development processes.
Responsibilities
- Design, develop, and maintain Java/Spring Boot microservices and React microfrontend (MF) components on GCP — following established service patterns, the Medecision MF architecture (runtime composition, atomic design), and a shared component library
- Build full-stack features end-to-end: RESTful APIs (service contracts, versioning, multi-tenant isolation) on the backend; TypeScript, Tailwind CSS on the frontend
- Implement event-driven workflows using Pub/Sub
- Own GitLab CI/CD pipelines end-to-end: build, lint, test, containerize (Docker), and deploy services and MF bundles to GCP; write JUnit 5/Mockito (backend) and Jest/React Testing Library (frontend) tests; author Storybook stories (story-first); use Datadog for backend and RUM observability
- Collaborate with on-shore and off-shore teams, architects, and tech leads to ensure on-time delivery and best engineering practices
- Engage proactively in the triage and resolution of escalated production issues — diagnosing failures, investigating root causes, and driving durable fixes with a sense of urgency, clear communication to stakeholders, and a commitment to preventing recurrence
- Follow and comply with all security policies and procedures established by the organization, including adherence to HIPAA and HITRUST regulations
- Design and develop backend services across core Clinical Intelligence modules — including real-time and automated rule execution, decision rules, program and population management, journey and campaign orchestration, and communications delivery
- Integrate with platform services (Population Data Service, Dictionary Service, Communication Service) and external partners; implement FHIR R4 integrations and healthcare interoperability workflows
- Contribute to the API and MCP layers — exposing platform APIs to AI agents with appropriate access controls and audit guardrails
- Use Claude Code as a primary productivity tool for code drafting, refactoring, test generation, and technical documentation — applying it with judgment, rigor, and accountability
- Contribute to building and exposing MCP-wrapped APIs that enable AI agents to safely interact with platform services
- Contribute to the team's shared AI knowledge base — validated prompts, skills, and workflows — and participate in the AI Champions community of practice
Skills
- Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience
- 5+ years of backend engineering building production microservices in Java with Spring Boot (Spring Data JPA, Security/OAuth2/JWT, OpenFeign, AOP, Actuator)
- 3+ years of production TypeScript/React experience with microfrontend architecture (Module Federation, runtime composition, shell/remote MF pattern); proficiency with Vite, Tailwind CSS, TanStack React Query, Storybook, and Jest/React Testing Library
- Proven ability to design and implement RESTful APIs — including service contracts, versioning, error handling, pagination, and multi-tenant isolation patterns
- Understanding of multi-tenant SaaS architecture patterns — tenant context propagation, per-tenant feature flags, and data isolation
- Hands-on experience with Google Cloud Platform services: Cloud Run, Cloud SQL (PostgreSQL), Pub/Sub, Firestore, BigQuery, Secret Manager, Cloud Logging, Artifact Registry
- Experience with event-driven architecture and asynchronous processing patterns — designing and consuming Pub/Sub topics, handling message ordering, deduplication, and dead-letter queues
- Excellent communication skills — able to articulate technical decisions, participate in design reviews, and collaborate effectively with cross-functional teams
- Use Claude Code as a primary productivity tool for code drafting, refactoring, test generation, and technical documentation — applying it with judgment, rigor, and accountability
- Contribute to building and exposing MCP-wrapped APIs that enable AI agents to safely interact with platform services
- Contribute to the team's shared AI knowledge base — validated prompts, skills, and workflows — and participate in the AI Champions community of practice
- Demonstrate a solid understanding of AI agentic concepts, capabilities, and limitations as they apply to software engineering workflows — including code generation, test scaffolding, and documentation
- Hands-on with Claude Code or equivalent as a daily productivity driver — not just experimentally; evaluates AI-generated code critically for hallucinations, logic errors, and security gaps
- Practical understanding of MCP (Model Context Protocol), or a strong willingness to learn it, for building tool wrappers that expose platform APIs to AI agents safely and with appropriate guardrails
- Applies AI with accountability: humans own decisions, agents own toil; zero tolerance for real PHI in AI-assisted workflows and commitment to mandatory HIPAA + AI training
- Knowledge of HIPAA and experience working in HIPAA-regulated product environments, including PHI handling, data classification, and audit requirements
- Hands-on experience with HAPI FHIR R4 and healthcare interoperability standards (HL7, FHIR resource mapping, validation workflows)
- Exposure to the AI agent runtime stack: agent orchestration frameworks such as LangGraph (Python), FastMCP
- Experience with infrastructure-as-code (Terraform) for provisioning and managing GCP environments
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