Member Technical Staff (Java, SQL, Platform Development)

Model N (Hyderabad, TS, India) Follow 2 hours ago

Premium Full-time REST SQL GraphQL Data Governance Agile Methodologies

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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.

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