What are the responsibilities and job description for the Lead Java Engineer (Core Java & AWS) - 14+ yrs (In-person Interview is required in Charlotte, NC) position at Jobs via Dice?
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Title: Lead Java Software Engineer (Core Java & AWS)
Location: Charlotte, NC (Hybrid - 3 days a week onsite)
Job Type: Contract
Note: **In-Person Interviews Required** (preferred locals)
Years of experience required: 14
Required Skills:
Core Java, AWS Lambda, RESTful APIs, Java, Microservices, Spring boot
Key Responsibilities:
Title: Lead Java Software Engineer (Core Java & AWS)
Location: Charlotte, NC (Hybrid - 3 days a week onsite)
Job Type: Contract
Note: **In-Person Interviews Required** (preferred locals)
Years of experience required: 14
Required Skills:
Core Java, AWS Lambda, RESTful APIs, Java, Microservices, Spring boot
Key Responsibilities:
- Lead backend development using Core Java (14 years experience required)
- Architect and implement microservices using Spring Boot, Spring Framework, Spring Data, and Spring Security
- Design and consume RESTful APIs; familiarity with SOAP/WSDL/XML is a plus
- Integrate AI/LLM workflows using Spring AI or similar frameworks
- Work with SQL databases, vector databases (e.g., Pinecone, Milvus, Redis Vector)
- Apply domain-driven design, CQRS, event sourcing, and reactive streams (e.g., Reactor, Spring WebFlux)
- Ensure secure development practices using OAuth, JWT, and OpenID Connect
- Participate in design discussions, mentor team members, and collaborate across cross-functional teams
- Support on-premises/hybrid deployments and legacy system modernization
- Implement feature toggles, A/B testing, and canary deployments
- Deep understanding of Java ecosystems and backend architecture
- Hands-on experience with Spring Cloud and microservices components (e.g., config server, service discovery, circuit breakers, API gateways)
- Strong grasp of software design patterns (repository, service, factory, strategy)
- Experience with AI orchestration, prompt chaining, and multi-agent workflows
- Familiarity with cloud-native AI deployments (AWS Lambda, Azure AI, Google Vertex AI)
- Ability to analyze and explain code-level decisions and patterns from past projects