Key Responsibilities
- Design and develop scalable Java/Spring Boot microservices, REST APIs, integrations, and event-driven services.
- Leverage Generative AI, LLMs, coding agents, Kiro, Agentic X, and similar tools to accelerate development.
- Translate business and technical requirements into effective prompts, specifications, and AI-assisted development tasks.
- Review, validate, debug, refactor, optimize, and productionize AI-generated code.
- Identify functional, architectural, security, performance, and maintainability issues in AI-generated solutions.
- Develop enterprise integrations using IBM MQ and Apache Kafka.
- Implement event-driven architectures, producers, consumers, queues, topics, and messaging patterns.
- Work with databases such as Oracle and MongoDB.
- Develop unit, integration, and component tests for both AI-generated and manually developed code.
- Integrate applications with CI/CD pipelines and DevOps processes.
- Collaborate with architects, engineering teams, and business stakeholders to deliver solutions rapidly and reliably.
- Continuously evaluate emerging AI development tools and contribute reusable prompts, accelerators, and AI-assisted engineering practices.
Mandatory Skills
Core Java & Microservices
- 8+ years of software engineering experience with strong hands-on development expertise.
- Excellent proficiency in Java and Spring Boot.
- Strong experience designing and developing enterprise-grade microservices.
- Strong understanding of REST APIs, distributed systems, integration patterns, and microservices architecture.
- Strong debugging, problem-solving, coding, and software design skills.
AI / Agentic Development
- 3-5 years of relevant AI / AI-assisted engineering experience.
- Practical experience with Generative AI, LLMs, AI coding assistants, or agentic development.
- Hands-on experience using AI models and coding agents to accelerate software development.
- Experience with, or strong willingness to work with, Kiro, Agentic X, and other AI coding/agentic platforms.
- Ability to effectively provide context, requirements, and specifications to AI models.
- Strong ability to review, validate, debug, refactor, optimize, and secure AI-generated code.
- Understanding of AI-assisted SDLC across design, development, testing, documentation, and code review.
Messaging & Integration
- Strong understanding of enterprise messaging and asynchronous integration.
- Hands-on experience with IBM MQ and Apache Kafka.
- Good understanding of event-driven architecture, queues, topics, producers, consumers, and messaging reliability patterns.
DevOps
- Good understanding of CI/CD and DevOps practices.
- Experience integrating Java applications into automated build, testing, and deployment pipelines.
- Familiarity with Git, build tools, and automated deployment practices.