We are looking for a Fresher Machine Learning Engineer who is passionate about AI and eager to begin their career in a fast-paced, innovation-driven environment. In this role, you will work with senior ML engineers and data scientists to build, test, and deploy machine learning solutions. This is a hands-on opportunity to learn industry best practices, understand end-to-end ML systems, and contribute to real-world projects from day one.
If you are enthusiastic about data, algorithms, and solving meaningful business problems with machine learning, we’d love to hear from you.
Assist in the development and training of machine learning models under supervision.
Work on data preprocessing, feature engineering, and exploratory data analysis.
Participate in model evaluation and tuning using appropriate metrics.
Support the deployment and integration of models into production environments.
Write clean, maintainable code and contribute to shared codebases using Git.
Collaborate with senior team members across data science and engineering functions.
Continuously learn and stay up to date with emerging ML trends, tools, and practices.
Education:
Bachelor’s degree (or final year) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
Experience:
Academic projects, Kaggle competitions, research papers, or internships involving machine learning.
Capstone or thesis work in ML or AI is a plus.
Proficiency in Python and foundational ML libraries (Scikit-learn, Pandas, NumPy).
Understanding of machine learning algorithms such as linear regression, decision trees, k-NN, etc.
Familiarity with Jupyter notebooks and basic data visualization (Matplotlib, Seaborn).
Basic SQL knowledge for data extraction and manipulation.
Good problem-solving skills and an eagerness to learn in a collaborative team setting.
Clear communication and documentation skills.
Exposure to deep learning frameworks (e.g., TensorFlow, Keras, or PyTorch).
Understanding of model deployment basics (e.g., using Flask/FastAPI or cloud ML services).
Awareness of MLOps concepts and tools (MLflow, Docker, GitHub Actions).
Participation in hackathons, AI clubs, or open-source contributions.