Member Technical Staff (Java, SQL, Platform Development)

Model N, Inc. is looking for a junior developer for its Life Science platform. The candidate should be hands-on with Java and related technologies, with a willingness to learn modern backend patterns, including AI-enhanced features. Job Responsibilities Develop features and code to specified requirements Identify and reuse existing components or define new reusabl e components Prioritize work assignments and deliver on schedule Write JUnit tests with adequate code coverage Participate in performance tuning when required Build and maintain RESTful APIs following platform standards Job Qualification 2-4 years of relevant software development experience Strong object-oriented design and Java programming skills Enterprise application development experience with J2EE application servers, preferably WebLogic or JBoss Experience with Oracle, SQL required; Performance tuning is a plus Good understanding of browser and servlet-based application structure Excellent communication and interpersonal skills Experience with Unix or Linux preferred Experience with Agile methodologies a plus Knowledge of Web API Development using REST / GraphQL is a plus Knowledge of SSO implementation using SAML/OpenID protocols is a plus Knowledge of CI/CD, containerization, and Orchestration technologies is a plus Willingness to work on any technology Fast learner, able to pick up new ideas and approaches quickly BE / BTech in Computer Science, or equivalent AI & LLM Skills (Preferred) Willingness to learn and implement features powered by AI-driven insights and recommendations Understanding of LLM (Large Language Model) concepts and their integration into backend systems—including API consumption, prompt optimization , and result handling for server-side operations. Familiarity with implementing intelligent business logic: recommendation engines, predictive analytics, auto-categorization of features/workflows, and smart defaults based on LLM analysis Understanding of data governance and privacy requirements for AI systems—PII handling, audit logging, data retention policies, and compliance with healthcare/life science regulations. Knowledge of monitoring and observability for AI-enhanced backends—tracking LLM API costs, inference latency, model performance degradation, and business impact metrics. Familiarity with fine-tuning or prompt engineering at the backend level to optimize LLM outputs for specific use cases and to enable A/B testing of AI features.