Key Responsibilities:

· Application Development: Design, develop, and maintain enterprise applications using Java, Spring Boot, Node.js, and JavaScript-based frameworks (e.g., React, Angular, Vue).

· Cloud Implementation: Work with cloud services (AWS, GCP or Azure) under the guidance of senior team members to deliver scalable and secure solutions.

· AI/ML Features: Assist in implementing AI/ML capabilities using cloud-based AI services and integrating them into enterprise workflows. Experience with Python is required.

· Copilot & AI Tools: Leverage AI-powered development tools to improve productivity and code quality.

· Best Practices: Follow established coding standards, cloud architecture patterns, and microservices/serverless approaches with a focus on quality, performance, and security.

· Collaboration: Work closely with senior developers, architects, and business stakeholders to understand requirements and deliver solutions aligned with project goals.

· Testing & Deployment: Participate in unit testing, debugging, and deployment activities, contributing to CI/CD pipelines in collaboration with DevOps teams.

· Continuous Learning: Stay updated on emerging cloud and AI capabilities and actively enhance your skills through mentorship and training.

Requirements

· Education: Bachelor's degree in computer science, Engineering, or related field.

Experience:

· 4–7 years in enterprise application development using Java, Spring Boot, Node.js, Python and JavaScript-based frontend frameworks.

· Practical exposure to cloud services and enterprise solution delivery.

· Basic to intermediate experience with AI/ML integration in applications.

· Familiarity with AI-assisted development tools.

Technical Skills:

· Java, Spring Boot, Node.js, Python

· Any JavaScript-based frameworks preferably React

· Exposure to integrating Generative AI into applications

· Cloud services (AWS, Azure, or GCP)

Certifications (Good to Have):

· Cloud Fundamentals (AWS, Azure, or GCP)

· AI-related certifications (e.g., AI-900, Azure AI Engineer Associate)

Good-to-Have Skills

· Cloud architecture patterns, microservices, serverless computing

· Docker & Kubernetes

· Unit testing & mocking frameworks

· Understanding of MLOps concepts for deploying and managing ML models.

· Familiarity with GraphQL or modern API design approaches.

Software Engineers

Java Software Engineers