Website:
halcer.com
Job details:
Senior Java Developer (Microservices, Spring Boot, Gen AI & AWS)
Locations: Pune | Mumbai | Hyderabad | Bangalore | Jaipur | Ahmedabad | Kolkata | Noida
About the Role
We are seeking an experienced Senior Java Developer with deep expertise in Microservices architecture, AWS cloud deployments, and Generative AI technologies. In this role, you will lead the design, development, and scaling of high-throughput backend services while embedding AI capabilities (LLMs, RAG, intelligent automation) into our enterprise cloud ecosystem.
Key Responsibilities
- Backend Development: Design, build, and maintain high-performance, fault-tolerant backend microservices using Java 17+, Spring Boot, and Spring Cloud.
- GenAI Integration: Build and integrate enterprise Generative AI solution workflows (Retrieval-Augmented Generation / RAG, LLM orchestration, Vector Databases) directly into existing Java/Microservices pipelines.
- Cloud Architecture: Architect, deploy, and optimize secure, cloud-native applications on AWS using services like ECS/EKS, AWS Bedrock, Lambda, API Gateway, S3, and RDS/DynamoDB.
- API & System Design: Design RESTful APIs and event-driven architectures leveraging messaging queues (Apache Kafka / AWS SQS/SNS) for high availability and low latency.
- DevOps & CI/CD: Establish automated CI/CD pipelines, containerization (Docker/Kubernetes), and observability dashboards (AWS CloudWatch, Prometheus) for end-to-end service deployment.
- Technical Leadership: Lead technical design discussions, enforce clean code standards, conduct code reviews, and mentor junior engineers.
Requirements & QualificationsMust-Have Technical Skills:
- Core Development: 3+ years of strong experience in Core Java (Java 11/17+), OOP principles, multi-threading, and design patterns.
- Frameworks: Extensive hands-on experience with Spring Boot, Spring Data JPA, Spring Security, and Spring Cloud.
- Generative AI: Practical experience integrating LLMs (OpenAI, Anthropic, Bedrock) or GenAI frameworks (LangChain4j, Spring AI, LlamaIndex, Vector DBs like pgvector/Pinecone) into backend applications.
- AWS Cloud: Strong experience with AWS infrastructure (ECS, EKS, Lambda, S3, RDS, DynamoDB, API Gateway, Secrets Manager).
- Microservices: Proficiency in service-to-service communication, distributed tracing, database design (SQL/NoSQL), and caching solutions (Redis).
Preferred Qualifications:
- AWS Certified Solutions Architect or AWS Certified Developer certification.
- Knowledge of Python for AI prototyping or scripting.
- Experience working in Agile/Scrum cross-functional environments.
Click on Apply to know more.
