🌐 Accellor is inviting applications from freshers for the Data & AI Engineer (Trainee) role through its Off Campus Drive 2025. This is an exciting opportunity for 2023, 2024, and 2025 graduates who are passionate about Artificial Intelligence, Machine Learning, and Data Engineering. As part of Accellor’s AI team, you will work on cutting-edge projects, collaborate with expert teams, and gain exposure to tools like TensorFlow, PyTorch, Python, and more. The position is based in Hyderabad and offers flexible work culture, professional development, and access to global innovation. Join Accellor and start your AI career journey today!
| Accellor | |
| Job Role | Data & AI Engineer (Trainee) |
| Qualification | Bachelor’s degree |
| Batch | 2023/2024/2025 |
| Experience | Freshers |
| Salary | ₹ 3 to 5 LPA |
| Location | Hyderabad |
| Last Date | ASAP |
| Apply Link Below | |
|---|---|
🎯 Eligibility Criteria
✅ Degree in Computer Science, Engineering, or a related stream
✅ Strong foundation in AI/ML, Deep Learning, Computer Vision, NLP
✅ Hands-on coding in Python, Java, or R
✅ Familiarity with TensorFlow, PyTorch, SQL
✅ Excellent analytical and team collaboration skills
🧾 Job Description
As a Data & AI Engineer Trainee, your role includes:
🤖 Assisting in designing and developing custom AI models and algorithms
🔗 Integrating AI solutions with business systems to boost automation
💽 Managing data infrastructure and flow for deployment
🧠 Keeping up with AI trends and improving current frameworks
🤝 Collaborating with multiple teams to align technical execution with business strategy
💡 Why Join Accellor?
🌍 Work on global projects in high-tech, healthcare, telecom, retail, and media
🤝 Engage in a collaborative and inclusive workplace culture
🏡 Flexible work schedules and remote-friendly policies
🎓 Upskill with continuous L&D programs, certifications, and soft skills workshops
🛡️ Enjoy excellent benefits like insurance, maternity leave, bonuses, and wellness programs
🏢 About Accellor
Accellor is a leading global technology solutions provider specializing in AI, cloud solutions, digital transformation, and data engineering. Known for innovation and agility, Accellor empowers industries across healthcare, telecom, and high-tech sectors with customized enterprise software, cloud-native development, and intelligent automation services for business scalability and transformation.
🧪 Selection Process
⚙️ The recruitment process includes:
1️⃣ Online Technical Test
2️⃣ Group Discussion
3️⃣ Technical Interview
4️⃣ Managerial Interview
📍 Note: Shortlisted candidates must attend in-person rounds at the Hyderabad office. No virtual interviews or rescheduling will be entertained. All communication will be through email, so check your inbox regularly.
📥 How to Apply?
All interested and eligible candidates can apply through the official link:
| Jobs by Qualification | |
| B.Tech | BBA |
| B.Sc | B.Com |
| M.Sc | M.Com |
| MCA | MBA |
| BCA | M.Tech |
❓ Top 5 Interview Questions & Answers
💬 Q1. What is the difference between supervised and unsupervised learning?
A: Supervised learning uses labeled data to train models (e.g., classification), while unsupervised learning identifies patterns in unlabeled data (e.g., clustering).
💬 Q2. What are common use-cases of AI in real-life applications?
A: AI is widely used in recommendation systems, fraud detection, chatbots, image recognition, voice assistants, and predictive analytics.
💬 Q3. How would you select the right ML algorithm for a project?
A: It depends on the data type, size, problem complexity, accuracy requirements, training time, and interpretability. I evaluate performance using cross-validation.
💬 Q4. Explain overfitting and how to prevent it.
A: Overfitting occurs when a model learns noise from the training data. It can be prevented using techniques like regularization, pruning, dropout, and cross-validation.
💬 Q5. How is Python beneficial for AI and ML development?
A: Python supports a vast ecosystem of libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch, making it ideal for data handling, model development, and deployment.
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