Senior Software Engineer

Job description:

Senior Software Engineer - Java Technologies & AI-Enabled SDLC

Role Summary We are seeking an experienced Senior Software Engineer specializing in Java technologies to design, develop, test, and deliver scalable, secure, and reliable enterprise applications. The ideal candidate will have strong hands-on expertise in Java, Spring Boot, REST APIs, microservices, cloud-native development, DevOps, and automated testing, with demonstrated ability to leverage AI and Generative AI technologies across the Software Development Life Cycle (SDLC) to improve engineering productivity, code quality, testing efficiency, and speed to market. This is a hands-on engineering role requiring strong technical ownership, problem-solving capabilities, collaboration, and a continuous-improvement mindset.

Key Responsibilities Java Engineering & Application Development:

• Design, develop, enhance, and maintain enterprise applications using Java and Java/Jakarta EE technologies.
• Develop scalable services and APIs using Spring, Spring Boot, REST APIs, and microservices architecture.
• Write clean, maintainable, reusable, and well-tested code following established engineering standards and design principles.
• Participate in solution design, technical design reviews, code reviews, and architecture discussions.
• Troubleshoot complex application, integration, performance, and production issues.
• Develop unit and integration tests and ensure adequate automated test coverage.
• Support application modernization, technical-debt reduction, and continuous improvement initiatives.
• Work closely with Product Owners, Business Analysts, QA, architects, DevOps engineers, and production support teams throughout the SDLC.

AI-Enabled SDLC:

• Actively use AI-assisted software engineering tools and Generative AI capabilities to accelerate development activities across the SDLC.
• Apply AI to improve requirements understanding, solution design, code generation, code refactoring, unit-test creation, test-data generation, defect analysis, documentation, and troubleshooting.
• Effectively use enterprise-approved AI coding assistants, such as GitHub Copilot, where available.
• Evaluate AI-generated code for correctness, security, maintainability, performance, and alignment with enterprise engineering standards rather than accepting generated code without validation.
• Identify repetitive engineering activities that can be automated or accelerated through AI agents, reusable prompts, workflows, or engineering accelerators.
• Contribute reusable AI-enabled engineering assets, patterns, and best practices that can be adopted by other development teams.
• Measure and demonstrate improvements achieved through AI adoption, including engineering productivity, development cycle time, quality, automation, and reduction of repetitive manual effort.
• Ensure AI-assisted development complies with applicable organizational standards for security, privacy, intellectual property, data protection, and responsible AI usage.

How you'll make an impact Required Technical Skills:

• Strong hands-on experience with Java, with solid understanding of object-oriented programming, data structures, design patterns, concurrency, exception handling, and performance optimization.
• Strong experience with Spring Framework and Spring Boot.
• Experience designing and developing RESTful APIs and microservices.
• Experience with persistence technologies such as JPA/Hibernate and relational databases.
• Strong understanding of SQL and database design concepts.
• Experience with build and dependency-management tools such as Maven or Gradle.
• Experience with automated unit and integration testing using technologies such as JUnit, Mockito, REST Assured, or equivalent frameworks.
• Experience with source-control and CI/CD practices using technologies such as Git and Jenkins, GitHub Actions, Azure DevOps, or equivalent platforms.
• Understanding of containerized application development using Docker and exposure to Kubernetes or similar orchestration platforms.
• Experience developing or supporting applications deployed to a major cloud platform such as Microsoft Azure, AWS, or Google Cloud Platform.
• Strong knowledge of secure coding, application security, logging, monitoring, exception management, and production-support practices.

AI & GenAI Skills:

• Practical experience using Generative AI or AI coding assistants within software engineering activities.
• Ability to create effective prompts and provide sufficient technical context to AI tools for software-development use cases.
• Experience using AI for code generation, refactoring, unit-test generation, documentation, debugging, code explanation, and engineering productivity.
• Understanding of the limitations and risks of AI-generated code and the importance of human review and validation.
• Familiarity with LLMs, prompt engineering, AI agents, Retrieval-Augmented Generation (RAG), APIs, and enterprise AI integration patterns is preferred.
• Exposure to technologies such as GitHub Copilot, Microsoft Copilot, Azure OpenAI, or comparable enterprise GenAI platforms is preferred.

Engineering & SDLC Expectations:

The Senior Software Engineer will be expected to take ownership across the engineering lifecycle: Requirements -> Design -> Development -> Code Review -> Unit Testing -> Integration Testing -> Deployment -> Production Support -> Continuous Improvement

The engineer should continuously look for opportunities to incorporate AI, automation, reusable components, and engineering accelerators into this lifecycle while maintaining appropriate quality and governance controls.

Qualifications:

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical discipline, or equivalent professional experience.
• Typically 5+ years of software engineering experience, with substantial hands-on Java development experience.
• Demonstrated experience delivering enterprise applications in Agile/Scrum environments.
• Strong analytical, debugging, and problem-solving capabilities.
• Ability to independently own complex development assignments from design through production implementation.
• Strong communication and collaboration skills, with the ability to work across business, development, QA, architecture, infrastructure, and support teams.

About you Preferred Qualifications:

• Experience with enterprise application modernization and migration from legacy or monolithic architectures to microservices and cloud-native architectures.
• Experience with event-driven or messaging technologies such as Kafka.
• Experience supporting highly available, business-critical production applications.
• Knowledge of API security, OAuth 2.0, OpenID Connect, application observability, and performance engineering.
• Experience developing AI agents or AI-enabled engineering accelerators.
• Experience integrating enterprise applications with LLMs or GenAI services.
• Relevant Java, cloud, DevOps, or AI certifications are an advantage.

Key Competencies:

• Strong technical ownership and accountability
• Hands-on Java engineering expertise
• AI-first and automation-first mindset
• Strong debugging and root-cause-analysis skills
• Secure and quality-focused engineering practices
• Ability to work effectively across global and distributed teams
• Continuous learning and technology adoption
• Clear communication and collaboration
• Focus on delivering measurable business value through technology

Success Measures:

• Delivery of reliable, maintainable, and secure Java applications
• Improved engineering productivity through practical adoption of AI
• Increased automation and reduced repetitive development effort
• Strong automated test coverage and reduced escaped defects
• Improved development and defect-resolution cycle times
• Creation and reuse of AI-enabled engineering accelerators
• Consistent adherence to architecture, security, quality, and SDLC standards

Skills

API, Java, Microservices, REST, Spring, Spring Boot