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.
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