Full Stack Java Engineer

Position: Full Stack Java Engineer

Location: Atlanta GA***Day 1 Onsite***

Duration: 1 Years

Full Stack Java Engineer (AI Integration AWS Angular)
Competencies: 8 years experience required
Digital : Amazon Web Service(AWS) Cloud Computing
Digital : Artificial Intelligence(AI)
Advanced Java Concepts
Java API Management & Microservices
Angular 13
Foundation : JavaScript
Role Description: Must-Haves are mandatory. Strong Pluses differentiate top candidates.
Core Full Stack Engineering (Must-Have):
Java Backend 58 years in Java (Java 11) Spring Boot Spring MVC Spring DataJPA REST API design pagination error handling versioning AuthenticationAuthorization (OAuth2OIDCJWT) Relational DB (PostgreSQLMySQLOracle) schema design indexing query optimization MessagingAsync (KafkaRabbitMQ) nice to have Performance tuning and profiling (memorythreadsGC)
Frontend Angular (v12) with TypeScript RxJS state management (NgRx or equivalent) Component-driven architecture lazy loading modularization Responsive UI accessibility basics (ARIA) API integration interceptors error retry strategies
Mobile (either one) Native Android (KotlinJava) or cross-platform (Flutter or React Native) Secure storage background tasks offline sync deep links Build release pipelines to app stores (if applicable)

AI Proficiency Scaffolding (Must-Have):
AI Integration Consumed LLM APIs (Azure OpenAIOpenAI AWS Bedrock Google Generative AI) from Java services Designed a clean AI service layer (abstractions to swap modelsproviders) Exposed AI via RESTGraphQL endpoints to Angularmobile clients
PromptContext Management Prompt templates (systemuserdeveloper roles) with versioningconfig Dynamic context injection (usersessionbusiness context) Token window management truncation strategies
Guardrails Validation Output schema enforcement (JSON schema regextagged formats) Safety filters (toxicity PII redaction) prompt injection mitigation Confidenceuncertainty handling human-in-the-loop patterns
Orchestration Patterns Prepost-processing pipelines multi-step chains RAG familiarity (vector stores embeddings) as user of services (not researcher) Fallbacks retries circuit breakers graceful degradation when AI failstimeouts
Cost Performance Observability Tokencost tracking and optimization (model choice prompt size caching) Streaming vs. non-streaming responses async processing Metricslogging for AI calls (latency error rates usage)
Frontend UX for AI Chatassistant patterns (streamed tokens partial updates) Editable AI suggestions retryfallback UX hallucination disclaimers
Cloud DevOps (Must-Have) CICD (GitHub ActionsAzure DevOpsJenkins) for backend and frontend Containerization (Docker) orchestration (Kubernetes) Secrets management (Key VaultSecrets Manager) Cloud exposure (AzureAWSGCP)deploying Java services frontends MonitoringTracing (OpenTelemetry App InsightsCloudWatchStackdriver)
Security Compliance Data (Must-Have) Secure coding (OWASP Top 10) input sanitization API security (OAuth2OIDCJWT CSRFCORS) Data privacy PII handling Responsible AI basics Encryption at restin transit KMS usage Audit trails for AI decisions and prompts (for enterprise traceability)
Testing Quality (Must-Have) Unitintegration tests (JUnitMock