Software Engineers

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.