🌟 Hexaware Technologies, a global leader in digital transformation, is inviting applications for the role of SPGET (Super Performing Graduate Engineer Trainee) from freshers graduating in 2025. This is an exciting opportunity to launch your career with one of India’s most admired IT companies, known for innovation, inclusivity, and high-impact tech. With a stunning salary package of ₹15 LPA and PAN India locations, this role offers exceptional growth, learning, and a chance to work with cutting-edge AI and Cloud technologies. Don’t miss the opportunity to work with 31,600+ Hexawarians across 28 countries!
| Hexaware | |
| Job Role | SPGET |
| Qualification | BE/BTech |
| Batch | 2025 |
| Experience | Freshers |
| Salary | ₹ 15 Lakhs per Annum |
| Location | PAN India |
| Last Date | 27 April 2025 10:00 PM |
| Apply Link Below | |
|---|---|
🎓 Eligibility Criteria
✅ Only 2025 pass-outs from BE / BTech
✅ Streams: CSE, IT, AI, Data Science, ECE, EEE, ETC, EIE, ISE
❌ Not eligible: M.E./M.Tech/MCA, Mechanical, Civil, and allied branches
✅ Minimum 75% or 7.5 CGPA in 10th, 12th & UG
✅ No active backlogs
✅ Willingness to relocate PAN India
✅ 3-Year Service Agreement
✅ Office-based role, 5 days a week
💼 Job Description
🎯 Join Hexaware’s elite innovation team and drive digital transformation as a Cloud and Generative AI Specialist. Your role includes:
💡 Driving Cloud and AI innovation
🧠 Solving complex business challenges with scalable solutions
🚀 Collaborating across teams and engaging clients
🛠️ Designing, testing, and implementing high-quality technical solutions
📊 Leveraging data analytics and ML/AI algorithms for intelligent solutions
🔄 Managing end-to-end project lifecycles and deployments
🧪 Building automation into software development and validation
🌟 Why Join Hexaware?
✨ Work with a future-ready digital transformation leader
🌍 Exposure to global clients and real-world challenges
🤝 Culture of innovation, inclusivity & people-first approach
📈 Learning opportunities through cutting-edge technologies
🏆 Be part of India’s biggest drive for college graduates
🏢 About Hexaware Technologies (SEO Optimized)
Hexaware is one of the fastest-growing global technology and business process services companies. With over 31,600 employees across 28 countries, Hexaware drives innovation in cloud computing, AI, automation, and digital customer experience. The company is committed to empowering talent and reshaping businesses through digital transformation.
🧠 Additional Information
🔐 This role offers an incredible compensation package of ₹15 LPA
💬 Communication and problem-solving are key traits for success
📚 Continuous learning and a growth mindset are highly encouraged
📍 Office-based job with relocation flexibility
📝 Selected candidates must sign a 3-year service agreement
📝 How to Apply?
🎯 All interested and eligible candidates can apply through the official Hexaware Careers portal using the link below:
| Jobs by Qualification | |
| B.Tech | BBA |
| B.Sc | B.Com |
| M.Sc | M.Com |
| MCA | MBA |
| BCA | M.Tech |
🗣️ Top 5 Interview Questions & Best Answers (SPGET Role)
💬 Q1: Explain the difference between Cloud Computing and Traditional IT Infrastructure.
👉 Cloud computing provides on-demand resources over the internet, enabling flexibility and scalability, while traditional IT involves on-premise infrastructure requiring higher maintenance and upfront investment.
💬 Q2: What is the importance of AI in business transformation?
👉 AI enables automation, data-driven insights, and predictive analytics, helping businesses optimize operations, enhance customer experience, and drive innovation.
💬 Q3: How do you ensure the scalability of a solution you design?
👉 I design modular and loosely-coupled systems, use cloud-native architectures, leverage auto-scaling in cloud environments, and ensure efficient code for performance optimization.
💬 Q4: What is the difference between supervised and unsupervised learning?
👉 Supervised learning uses labeled data to train models, while unsupervised learning identifies patterns in data without labels.
💬 Q5: Describe your approach to debugging a failed AI model deployment.
👉 I start with log analysis, validate data pipelines, test model inputs/outputs, check for environment or API errors, and use version control to roll back if needed.
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