About the Role
We are hiring fresh graduates from colleges with dedicated AI/ML, Data Science, or related specialization programs to join our AI/ML engineering team. This role is designed for candidates who have completed structured academic coursework in artificial intelligence and machine learning, and are looking to apply that foundation to real-world business problems. You will work alongside experienced engineers, contribute to live projects from day one, and follow a structured ramp-up plan to grow into an independent AI/ML developer.
Who This Role Is For
This opening is specifically targeted at students who:
• Are pursuing or have recently completed a Bachelor's or Master's degree with AI/ML or Data Science as a core specialization (e.g., B.Tech/B.E. in AI & ML, B.Tech CSE with AI specialization, B.Sc./M.Sc. Data Science, M.Tech AI/ML). • Have completed at least 3–4 academic courses in AI/ML topics — for example, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Computer Vision, or Statistical Learning. • Have built academic or capstone projects applying AI/ML to real datasets. • Are eager to transition from coursework to production-grade engineering practices.
Educational Qualification (MUST)
• Bachelor's or Master's degree from a recognized university with AI/ML, Data Science, or Computer Science (with AI/ML specialization) as the primary stream. • Minimum 65% / 6.5 CGPA in graduation (or as per company policy). • College curriculum must include dedicated AI/ML coursework. Candidates should be able to share their academic transcript listing relevant subjects.
Required Academic Coursework Candidates must have studied (and earned credit in) most of the following: • Mathematics for ML — Linear Algebra, Probability & Statistics, Calculus. • Core Machine Learning — supervised and unsupervised learning, model evaluation, regularization, bias-variance trade-off. • Deep Learning — neural networks, backpropagation, CNNs, RNNs, or transformers. • Programming Fundamentals — Python, data structures and algorithms, object-oriented programming. • Database Systems — SQL, relational database concepts. • At least one elective in NLP, Computer Vision, Reinforcement Learning, or Time Series Analysis is preferred.
Required Skills • Strong Python programming — comfortable writing functions, classes, and modular code (not just notebook scripting). • Working knowledge of at least one ML library: scikit-learn, TensorFlow, Keras, or PyTorch — applied in coursework or projects. • Solid grasp of core ML concepts: supervised vs unsupervised learning, overfitting/underfitting, train-test-validation splits, cross-validation, common evaluation metrics (accuracy, precision, recall, F1, RMSE, AUC). • Hands-on with data preprocessing — handling missing values, encoding categorical variables, normalization, feature scaling, and basic feature engineering. • Practical experience with at least 3 algorithm families: linear/logistic regression, decision trees and ensembles, clustering, or neural networks. • Familiarity with SQL — writing SELECT queries, joins, group-by, and basic aggregations. • Comfortable with Git and GitHub — clone, commit, push, pull, branches. • Tools: Jupyter Notebooks, Google Colab, or VS Code. • Ability to write clean, readable, and well-documented code. • Strong communication skills — able to explain technical work to peers and mentors.
Required Project / Practical Experience Candidates must showcase at least 2 AI/ML projects from coursework, capstone, internships, or self-driven work. Projects can include: • Final-year capstone or major project. • Mini-projects from ML/DL coursework with real or open datasets. • Internship work in AI/ML/Data Science. • Kaggle competition entries or hackathon submissions. • Open-source contributions or independent learning projects published on GitHub. For each project, candidates should be ready to discuss: • Problem statement and dataset used. • Preprocessing and feature engineering approach. • Models tried and the final model selected. • Evaluation metrics, results, and key learnings.
Key Responsibilities • Assist in building, training, and evaluating ML models under the guidance of senior engineers. • Perform data preprocessing, exploratory data analysis (EDA), and feature engineering on real datasets. • Write clean, modular Python code for ML pipelines and contribute to the team's shared codebase. • Run experiments, document results, and present findings clearly to the team. • Participate in code reviews and follow standard engineering practices including version control and testing. • Continuously learn and adopt new AI/ML tools, frameworks, and best practices. • Collaborate with cross-functional teams — product, data engineering, and senior ML developers — on assigned project tasks.
Nice-to-Have • Internship experience in an AI/ML, Data Science, or Software Engineering role. • Exposure to Generative AI, Large Language Models, prompt engineering, or frameworks like LangChain or Hugging Face. • Familiarity with cloud platforms — AWS, GCP, or Azure (free-tier or student credits experience is acceptable). • Awareness of MLOps tools — MLflow, Airflow, or DVC. • Active GitHub profile with personal AI/ML projects. • Kaggle competition participation, AI hackathon wins, or paper presentations at student conferences. • Additional certifications from DeepLearning.AI, Coursera, NPTEL, or AWS/Google ML programs. • Contribution to open-source AI/ML repositories.
