Responsibilities of the Candidate:

Design, develop, and maintain scalable backend and frontend applications using Java and JavaScript (Node.js, Angular, or similar frameworks).
Leverage AWS cloud services including Lambda, EC2, S3, API Gateway, RDS, ECS, and CloudFormation to deliver resilient cloud-native solutions.
Write clean, testable, and maintainable code, adhering to modern software engineering practices.
Use AI-assisted development tools (GitHub Copilot, Amazon CodeWhisperer, Claude Code) to accelerate coding, automate test generation, and streamline code reviews.
Apply prompt engineering to solve realtime problems.
Participate actively in Agile practices including sprint planning, daily standups, and retrospectives.
Collaborate cross-functionally with product managers, designers, and engineering peers to define, develop, and deliver new features — identifying opportunities where AI can add value to the product and development process.
Monitor application performance, troubleshoot issues, and drive optimizations to ensure high availability and responsiveness — leveraging AI-powered log analysis and anomaly detection to accelerate root cause identification.
Engage in a rotating support schedule (2-sprint rotation) and participate in on-call responsibilities.
Utilize observability and monitoring tools to ensure system reliability and proactive issue detection, incorporating intelligent alerting and automated triage where applicable.

Requirements

1–2 years of professional software development experience.
Strong experience in Java and in JavaScript frameworks (Angular, Node.js, or equivalent) is required.
Hands-on experience deploying and operating applications using AWS services in production environments.
Solid understanding of RESTful API design, asynchronous data handling, and event-driven architecture.
Familiar with DevOps best practices including "Push on Green", version control using Git, and automated deployments.
Experience working with observability tools for logging, monitoring, and alerting.
Exposure to AI/ML concepts and a demonstrated interest in applying generative AI to real-world engineering problems — whether through personal projects, coursework, or professional experience.
Basic understanding of vector databases, embeddings, or retrieval-augmented generation (RAG) patterns is desirable but not required — willingness to learn is essential.
Strategic thinker with strong problem-solving skills and a passion for continuous learning and improvement, particularly around emerging AI technologies and their practical applications.
Effective communicator with a collaborative mindset, able to work closely with cross-functional teams.
Bachelor's degree in Computer Science, Engineering, or a related field; advanced degrees are a plus.
Ability to thrive in a dynamic, fast-paced environment and adapt to changing technologies and priorities, including the rapidly evolving AI landscape.