Full Stack Java Engineer

Duration: 1 Years
Full Stack Java Engineer (AI Integration + AWS + Angular)

Position:
Full Stack Java Engineer

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

Duration: 1 Years

• 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:
  Java Script

Role

Description:

Must-Haves are mandatory. Strong Pluses differentiate top candidates.

• Java Backend: 5-8 years in Java (Java 11)
• Spring Boot, Spring MVC, Spring Data JPA
• REST API design, pagination, error handling, versioning
• Authentication/Authorization (OAuth2/OIDC/JWT)
• Relational DB:
  Postgre
  
  SQL, MySQL, Oracle
• Schema design, indexing, query optimization
• Messaging:
  Async (Kafka, Rabbit
  
  MQ)
• Nice to have:
  Performance tuning and profiling (memory, threads, GC)

• Type Script, RxJS, state management (NgRx or equivalent)
• Component-driven architecture, lazy loading, modularization
• Responsive UI, accessibility basics (ARIA)
• API integration, interceptors, error retry strategies

• Native Android (Kotlin/Java) or cross-platform (Flutter or React Native)
• Secure storage, background tasks, offline sync, deep links
• Build release pipelines to app stores (if applicable)

• AI Integration:
  Consume LLM APIs (Azure OpenAI, OpenAI, AWS Bedrock, Google Generative AI) from Java services
• Design a clean AI service layer (abstractions to swap model providers)
• Expose AI via REST/Graph
  
  QL endpoints to Angular/mobile clients
• Prompt Context Management:
  Prompt templates (system/user/developer roles) with versioning config
• Dynamic context injection (user session, business context)
• Token window management, truncation strategies
• Guardrails:
  Validation output schema enforcement (JSON schema, regex tagged formats)
• Safety filters (toxicity, PII redaction), prompt injection mitigation
• Confidence/uncertainty handling, human-in-the-loop patterns
• Orchestration patterns:
  Pre/post-processing pipelines, multi-step chains, RAG familiarity (vector stores, embeddings)
• Fallbacks, retries, circuit breakers for graceful degradation when AI failures, timeouts
• Cost, Performance, Observability:
  Token cost tracking and optimization (model choice, prompt size, caching)
• Streaming vs. non-streaming responses, async processing
• Metrics and logging for AI calls (latency, error rates, usage)
• Frontend UX for AI chat assistant patterns (streamed tokens, partial updates)
• Editable AI suggestions, retry/fallback UX, hallucination DISCLAIMERS

• CICD:
  Git Hub Actions, Azure Dev Ops, Jenkins for backend and frontend
• Containerization:
  Docker, orchestration:
  Kubernetes
• Secrets management:
  Key Vault, Secrets Manager
• Cloud exposure:
  Azure, AWS, GCP – deploying Java services, frontends
• Monitoring, tracing:
  Open Telemetry, App Insights, Cloud Watch, Stackdriver

• Secure coding (OWASP Top 10), input sanitization, API security (OAuth2/OIDC/JWT, CSRF, CORS)
• Data privacy, PII handling, Responsible AI basics
• Encryption at rest and in transit, KMS usage
• Audit trails for AI decisions and prompts (enterprise traceability)

• Unit integration tests (JUnit, Mock...)