Java Developer

Java Architect AWS Migration & Modernization

Experience: 12+ Years
Location: Fort Mills, SC
Duration- FTE ANd C2C
Role Type: Architect / Lead / Developer

Job Overview

Seeking a strong Developer with deep experience in cloud migration, application modernization, and AWS-native architectures. Must be hands-on with Java, microservices, and AWS services, and able to lead modernization of legacy Java applications and cloud-native builds.

Key Responsibilities

• Architect and lead migration of Java applications to AWS, including Lift & Shift and modernization.
• Break down legacy monolithic apps into microservices using Java/Spring Boot.
• Design cloud-native solutions using AWS services (Lambda, SQS, SNS, Step Functions, S3, RDS, DynamoDB, ALB).
• Create high-level and low-level architecture, integration patterns, and modernization strategy.
• Guide engineering teams on Java best practices, Spring ecosystem, distributed systems, and performance optimization.
• Enable DevOps automation using CI/CD, Docker, Kubernetes (EKS), and observability (ELK, Prometheus, Grafana, OpenTelemetry).
• Ensure security, scalability, and cost optimization across AWS workloads.

Required Skills

• 12+ years in Java (11/17/21) with strong OOP, multithreading, JVM tuning.
• Expertise in Spring Boot, Spring Cloud, Spring Security, JPA/Hibernate.
• Strong in REST APIs, microservices, event-driven architecture.
• Hands-on with AWS: Lambda, Step Functions, SQS, SNS, S3, Glue, RDS PostgreSQL, DynamoDB.
• Experience migrating applications between AWS accounts / Landing Zones.
• Strong with SQL (PostgreSQL/MySQL) and NoSQL (DynamoDB, Redis).
• Experience with Docker, Kubernetes/EKS, CI/CD (Jenkins/GitHub Actions/GitLab), and IaC (Terraform/CloudFormation).
• Testing tools: JUnit 5, Mockito, Testcontainers.

Preferred Qualifications

• AWS Solution Architect/Developer/DevOps certification.
• Experience with DDD, Clean Architecture, CQRS.
• Strong leadership, communication, and architectural decision-making.

Nice-to-Have Skills

• Exposure to Generative AI concepts (LLMs, embeddings, simple RAG patterns).
• Basic understanding of prompt engineering and LLM integration.
• Familiarity with Agentic AI concepts (AI agents, workflow automation).
• Some experience with vector databases (OpenSearch, Pinecone, Redis Vector).
• Awareness of AWS Bedrock services and foundational models (Claude, Llama, Titan).