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)

Job description