🌟 Avasoft is inviting applications for the role of AI/ML Trainee Engineer through its Off Campus Drive 2025. This is a golden opportunity for B.E/B.Tech graduates from the 2023, 2024, and 2025 batches to work on cutting-edge Artificial Intelligence and Machine Learning projects. With a lucrative salary package of ₹8–10 LPA and job location at the vibrant tech hub of Navalur, Chennai, this role is ideal for aspiring tech enthusiasts who want to grow in a fast-paced and innovative environment. Apply now and elevate your career with one of the fastest-growing digital transformation companies!
| Avasoft | |
| Job Role | AI/ML Trainee Engineer |
| Qualification | B.E/B.Tech |
| Batch | 2023/2024/2025 |
| Experience | 0 – 1 Year |
| Salary | Rs. 8 – 10 LPA |
| Location | Navalur, Chennai |
| Last Date | 15 April 2025 |
| Apply Link Below | |
|---|---|
✅ Eligibility Criteria
🎓 Must possess B.E/B.Tech degree in a relevant field
🧠 Experience level: 0 to 1 year
🛠️ Skills required: SQL, Python, Applied Machine Learning (ATML)
🔄 Must be highly adaptable and flexible
💼 Strong business acumen is a plus
💼 Job Description
📊 Database Management & SQL
🗃️ Strong knowledge of relational databases and ER modeling
🔎 Ability to write scalable and efficient SQL queries for business use
🐍 Python Programming
📜 Proficiency in core Python: control structures, file handling, functions, exceptions
🔍 Skilled in debugging, code analysis, and performance optimization
✨ Preferred: Experience in OOP, Flask, FastAPI
🤖 AI & Machine Learning
🧠 Solid foundation in AI concepts like prompt engineering and Retrieval-Augmented Generation (RAG)
🧪 Experience with ML lifecycle: preprocessing, training, evaluation, and deployment
📸 Preferred: Hands-on with image analysis, document intelligence, video analytics
🧬 Bonus: Familiarity with model fine-tuning, distillation, and pretraining techniques
☁️ Cloud & Deployment (Preferred)
☁️ Basic understanding of deploying AI/ML models on AWS or Azure platforms
🚀 Why Join Avasoft?
🌐 Exposure to real-world AI/ML projects and new-age technologies
📈 Competitive salary with rapid growth opportunities
🏢 Work in Chennai’s thriving IT corridor
🎓 Continuous learning and training support
🏆 Work culture focused on innovation, impact, and inclusivity
🏢 About Avasoft
Avasoft is a leading digital transformation partner specializing in AI/ML, cloud computing, and data-driven enterprise solutions. With a strong commitment to innovation and engineering excellence, Avasoft empowers fresh talent to build impactful technology solutions. The company offers exceptional career paths and immersive learning experiences in the tech domain.
📂 Additional Information
📌 Required Technical Skills:
🔹 SQL proficiency🔹 Python (Core, OOP, API frameworks like Flask/FastAPI)
🔹 AI/ML fundamentals and pipelines
🔹 Optional: Cloud knowledge (AWS/Azure), video and image processing
📌 Preferred Knowledge Areas:
📍 Prompt engineering, RAG📍 Web scraping
📍 Document & video analytics
📍 Model optimization
🧾 How to Apply?
🖱️ Interested and eligible candidates can apply online using the link below before 15 April 2025.
| Jobs by Qualification | |
| B.Tech | BBA |
| B.Sc | B.Com |
| M.Sc | M.Com |
| MCA | MBA |
| BCA | M.Tech |
🤝 Top 5 Interview Questions & Sample Answers
❓ Q1: What is the difference between supervised and unsupervised learning?
💬 Supervised learning uses labeled data for training models, while unsupervised learning finds hidden patterns in data without labels.
❓ Q2: Explain how overfitting can be avoided in ML models.
💬 Overfitting can be prevented using techniques like cross-validation, pruning, regularization, and using simpler models.
❓ Q3: How would you optimize a slow SQL query?
💬 By using proper indexing, avoiding unnecessary joins, analyzing execution plans, and limiting data retrieval.
❓ Q4: What are some use cases of RAG (Retrieval-Augmented Generation)?
💬 RAG is useful in knowledge-based question answering, document summarization, and chatbot development.
❓ Q5: How would you deploy a Python-based ML model on the cloud?
💬 Using frameworks like Flask or FastAPI to wrap the model, containerizing with Docker, and deploying via AWS/Azure services.
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