Software Developer 9815-0712

Responsibilities:

• Design build and maintain secure scalable Java services and APIs using Spring Boot.
• Translate technical requirements into production-grade application code integration logic and robust data access layers.
• Write clean testable Java (unit integration regression) contribute to CI/CD pipelines and support automated deployments.
• Design build and optimize data workflows including SQL queries ETL logic and caching for reliability integrity and performance in production.
• Collaborate with data engineers and analysts to ensure service-layer alignment with enterprise data models and reporting needs.
• Diagnose and resolve production issues (performance defects incidents); participate in on-call / support rotations as needed.
• Review code enforce engineering standards document solutions and mentor intermediate developers.
• Collaborate with architects QA product owners and business SMEs in an iterative / Agile delivery model to plan scope and land increments.
• Apply AI/ML capabilities (LLMs retrieval-augmented generation classic ML models) to enhance existing Java services where appropriate.
• Design and consume AI-backed services (e.g. classification summarization recommendations reasoning assistants) through secure REST integrations.
• Support model lifecycle activities such as monitoring output quality drift awareness and safe auditable usage of AI features.

General Skills:

• Strong Java and Spring Boot experience building enterprise services at scale (API design dependency management error handling observability performance tuning).
• Advanced SQL fluency (Oracle MySQL PostgreSQL) complex joins window functions data validation and query optimization.
• Working knowledge of data modeling ETL/ELT pipelines and API-driven data integration.
• Hands-on experience with Git automated testing secure coding practices code reviews and CI/CD pipelines.
• Experience deploying containerized services (Docker) to managed platforms or Kubernetes; comfort with production-grade runtime concerns (logging metrics alerts).
• Ability to integrate third-party / platform services and expose them through hardened APIs.
• Familiarity with responsible use of AI services in production: PII handling privacy controls auditability bias/safety considerations.
• Ability to translate business needs into technical designs and incremental deliverables; strong troubleshooting and communication skills.
• Asset: exposure to AI/ML development workflows (Python data prep prompt design vector search etc.); ability to partner with data/AI specialists and embed their outputs in Java services.

Desirable Skills:

• Integration of AI assistants / copilots / LLM features (for example: routing a user request from a Java service to Azure OpenAI Copilot Bedrock etc.).
• Retrieval-augmented generation patterns (prompt construction grounding with vector stores such as FAISS pgvector Azure AI Search).
• Experience with analytics and data visualization tools (Power BI Looker or Tableau) to surface operational and model KPIs.
• Understanding of data governance and quality frameworks (metadata management lineage audit trails).
• Experience in case management / benefits administration domains (for example Curam or similar social services platforms).
• Experience with secure handling of sensitive client data (privacy masking role-based access audit trails).

Must Have:

• 7 years hands-on Java development in an enterprise environment including Spring Boot REST API design integration patterns and production support / incident management.
• Strong SQL and data handling expertise: capable of analyzing schemas building optimized queries integrating APIs with data stores and enforcing data quality in service logic.
• Proven experience supporting applications in production: triaging defects analyzing incident root cause applying hotfixes improving resiliency and performance.
• Ability to consume and operationalize AI services: call LLM endpoints handle prompt/response patterns enforce guardrails and log usage safely.
• Practical understanding of core ML / LLM concepts (supervised vs unsupervised learning prompt engineering retrieval drift) sufficient to collaborate with data/AI teams and ship AI-enabled features.
• Comfort working in a secure governed environment (privacy PII protection access control auditability).

Technical Expertise 80%

• Enterprise Java delivery: 7 years building secure scalable services and APIs using Java and Spring Boot in a production environment.
• SQL data access and amp; integration: Strong experience working with relational databases (Oracle SQL Server MySQL) including complex joins query optimization data integrity enforcement and schema-driven design. Ability to collaborate with data teams on modeling ETL and API-based data integration.
• Data engineering collaboration: Practical understanding of data pipelines transformations and validation workflows that support service reliability and analytics. Experience building data-driven logic in applications (e.g. caching persistence aggregation or event-driven updates).
• Production support and amp; incident management: Proven track record diagnosing and resolving application issues across dev/test/prod; strong root-cause analysis defect remediation hotfix coordination and performance tuning using logs metrics and APM tools.
• API design and amp; integration: Design build and consume REST services; manage authentication secrets and payload validation; integrate with internal and external systems including data and AI services.
• CI/CD and amp; engineering discipline: Hands-on experience with Git automated testing code reviews and build/deploy pipelines; containerization using Docker. Experience deploying to managed runtime platforms or Kubernetes is an asset.
• Secure development: Ability to build services with proper access control auditing error handling and resiliency; familiarity with privacy data governance and PII protection requirements.
• Documentation and amp; standards: Produces clear technical documentation follows architectural guidance and contributes to shared patterns and reusable components.
• AI/ML platform integration (20% of role): Ability to safely call AI services (e.g. Azure OpenAI Bedrock Copilot) from Java applications handle prompt/response patterns and apply guardrails for safety privacy and auditability.
• Foundational AI skills: Familiarity with modern LLM and retrieval-augmented generation patterns (prompt construction retrieval via vector stores such as FAISS pgvector or Azure AI Search tool/function calling basic fine-tuning/LoRA).
• Data handling for AI features: Ability to work with structured and unstructured data perform quality checks and manage feature-ready datasets that power AI-driven functionality.

Methodology Testing and amp; Troubleshooting 20%

• Agile delivery: Comfortable working in iterative sprints with product owners QA architects data engineers and business partners; able to refine requirements into deliverable increments.
• Quality mindset: Designs and writes unit integration and data-validation tests; supports automated regression and non-functional testing (performance stability).
• Structured problem solving: Strong debugging discipline; able to analyze code logs and data flows to propose pragmatic solutions and identify when AI or data-driven automation adds business value.
• Risk and amp; issue management: Anticipates delivery and production risks including data integrity issues; raises them early and drives mitigation actions.
• Communication and amp; teamwork: Clear written and verbal communication; able to lead or contribute to design discussions walkthroughs and knowledge transfer sessions across development and data teams.

1. 7 years hands-on Java development in an enterprise environment including Spring Boot REST API design integration patterns and and nbsp;production support / incident management.
2. Strong SQL and data handling expertise: capable of analyzing schemas building optimized queries integrating APIs with data stores and enforcing data quality in service logic.
3. Proven experience supporting applications in production: triaging defects analyzing incident root cause applying hotfixes improving resiliency and performance.
4. Ability to consume and operationalize AI services: call LLM endpoints handle prompt/response patterns enforce guardrails and log usage safely.
5. Practical understanding of core ML / LLM concepts and nbsp;(supervised vs unsupervised learning prompt engineering retrieval drift) sufficient to collaborate with data/AI teams and ship AI-enabled features.
6. Comfort working in a secure governed environment (privacy PII protection access control auditability).