Java Full Stack/ Python GenAI Developer

Infosys is a global leader in next-generation digital services and consulting. We enable clients in 56+ countries to navigate their digital transformation. With over three decades of experience in managing the systems and workings of global enterprises, we expertly steer our clients through their digital journey. We do it by enabling the enterprise with an AI-powered core that helps prioritize the execution of change. We also empower the business with agile digital at scale to deliver unprecedented levels of performance and customer delight. Our always-on learning agenda drives their continuous improvement through building and transferring digital skills, expertise, and ideas from our innovation ecosystem.

Java Full Stack Developer - Analyzing user requirements, envisioning system features and functionality.

• Design, build, and maintain efficient, reusable, and reliable Java codes by setting expectations and features priorities throughout development life cycle
• Identify bottlenecks and bugs, and recommend system solutions by comparing advantages and disadvantages of custom development
• Contributing to team meetings, troubleshooting development and production problems across multiple environments and operating platforms
• Understand Architecture Requirements and ensure effective Design, Development, Validation and Support activities
• In-depth knowledge of design issues and best practices
• Solid understanding of object-oriented programming
• Familiar with various design, architectural patterns and software development process.
• Experience with both external and embedded databases
• Creating database schemas that represent and support business processes
• Implementing automated testing platforms and unit tests

Python _GenAI/ML Developer- Design, develop, and maintain Python-based AI/ML and GenAI services aligned to product and business needs.

• Build and optimize RESTful APIs using FastAPI/Flask for model inference and GenAI workflows.
• Implement GenAI solutions (prompting, orchestration, evaluation) and integrate them into applications responsibly.
• Develop and maintain ML pipelines for data preparation, training, validation, and inference using NumPy and Pandas.
• Apply MLOps practices to enable reproducible experiments, versioning, CI/CD, monitoring, and reliable deployments.
• Collaborate with stakeholders to translate requirements into technical designs, estimates, and deliverables.