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.
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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) aEUR" 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.
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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).
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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.
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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.

Practi

cal 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).