Software Engineer

We are looking for a Full Stack Software Engineer to design, build, and operate scalable, mission-critical healthcare applications. This is abackend-first full-stack role (~70% backend / 30% frontend)where you will primarily work on Java + Spring Boot microservices, while contributing toReact-based UI layers to deliver complete end-to-end features. You will also operate in anAI-first engineering environment, where tools like GitHub Copilot, Windsurf, and Claude are used to accelerate development, with a strong expectation that you validate, refine, and productionize outputs to meet the security by design with enterprise-grade standards

The candidate will have responsibilities across the following functions:

Full Stack Development:

• Design and buildscalable microservicesusingJava, Spring Boot, and REST APIs.
• Define clear API contracts (OpenAPI/Swagger) with consistent error handling and versioning.
• Implementevent-driven architecturesusing Kafka or equivalent messaging systems.
• Build high-performance data access layers usingJPA/Hibernate and Oracle/Postgres.
• Develop and enhance UI components using React.js and TypeScript.
• Build integration layers and admin UIs to support backend workflows.
• Ensureend-to-end flow validation(UI, API, DB events).
• Ensure production-grade reliability: transaction safety, idempotency, and error recovery.

AI-First Engineering Mindset (Critical):

• Actively use AI tools (e. g., GitHub Copilot, Windsurf, Claude or any other McKesson-approved tools)to: Accelerate coding, refactoring, and test generation. Improve developer productivity without compromising maintainability.
• Critically evaluate AI-generated code for: Correctness and edge cases, Security and data handling risks, Performance and scalability
• Apply AI responsibly in: Code generation, debugging, and clear documentation, Unit Test case creation and coverage improvement
• Design systems that are: AI-friendly (clear APIs, structured data, consistent contracts), Observable and diagnosable using logs, metrics, and traces, Security by design

Engineering Quality and Delivery:

• Write clean, maintainable, well-tested code(unit + integration tests).
• Participate in code reviews, design discussions, and architecture decisions.
• Work in Agile teams with strong accountability and ownership of delivery.
• Diagnose and resolve production issues using observability tools, such as Dynatrace.

Platform and System Thinking:

• Build services that are reusable, Well-documented, and easy for other products to integrate.
• Follow best practices in: API design, Microservices architecture, CI/CD and DevOps pipelines.

Minimum Requirement:

• Degree or equivalent relevant experience.
• 2-5 yearsof software development experience in product/enterprise environments.
• Strong hands-on experience with: Java (17+) and Spring Boot, REST API design and microservices architecture.
• Experience with React.js + TypeScript(at least 1-2 years).
• Solid experience with: SQL and relational databases (Oracle/Postgres), ORM tools (Hibernate/JPA).
• Experience with: Event-driven systems (Kafka or similar), Caching (Redis or similar), CI/CD pipelines and Git-based workflows, Automated testing practices.
• Strong debugging and production issue analysis skills.

AI-First Expectations (Non-Negotiable)

Candidates must demonstrate:

• Practical use of AI coding assistants in development workflow.
• Ability to review, validate, and production-harden AI-generated code.
• Understanding that AI accelerates development.
• Engineers are still accountable for design, correctness, and quality.
• Foundational: Clear written and verbal communication ability to document services and explain technical decisions.

Good-to-Have Skills:

• Healthcare domain experience.
• Familiarity with:
• FHIR / HL7 / C-CDA standards
• HIPAA compliance and PHI/PII handling
• Experience with:
• Databricks / data pipelines
• Elasticsearch / Redis
• Docker / Kubernetes

What Success Looks Like:

• Deliver production-ready features end-to-end, not just code.
• Build systems that are scalable, observable, and reusable.
• Improve team productivity using AI without degrading quality.
• Contribute to raising engineering standards across the team.