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 reusable 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.