Job Description

:
HM Note: This onsite contract role is in office every day at the manager's discretion. Candidate resumes must include first and last name, email and telephone contact information.
Description
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).

Skills
Experience and Skill Set Requirements
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 & 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 & 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 & 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 & 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 & 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 & 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 & issue management: Anticipates delivery and production risks, including data integrity issues; raises them early and drives mitigation actions.
• Communication & teamwork: Clear written and verbal communication; able to lead or contribute to design discussions, walkthroughs, and knowledge transfer sessions across development and data teams.

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).

Skills Required