AI/ML Engineer Intern
Department: Engineering
Experience: 6 months
- Build, train, and evaluate machine learning models for various use cases such as prediction, classification, and recommendation systems.
- Work with large datasets to clean, preprocess, and transform data for model training.
- Implement and experiment with algorithms using libraries like Scikit-learn, TensorFlow, or PyTorch.
- Assist in deploying ML models into production environments using APIs or batch pipelines.
- Collaborate with engineering teams to integrate ML solutions into product workflows.
- Perform exploratory data analysis (EDA) and visualize insights using tools like Pandas, Matplotlib, or Seaborn.
- Optimize model performance through tuning, feature engineering, and validation techniques.
- Stay updated with the latest advancements in AI/ML and suggest improvements to existing systems.
- Document experiments, models, and workflows for reproducibility and collaboration.
- Participate in Agile ceremonies including sprint planning, reviews, and retrospectives.
- Bachelor’s degree (or currently pursuing) in Computer Science, Engineering, Data Science, or a related field.
- 0–6 months of experience or strong academic/project exposure in machine learning or data science.
- Strong programming skills in Python.
- Understanding of machine learning concepts such as supervised and unsupervised learning, regression, classification, and clustering.
- Familiarity with ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
- Basic knowledge of data structures, algorithms, and statistics.
- Experience with data manipulation and analysis using Pandas or NumPy.
- Knowledge of Git and version control workflows.
- Strong problem-solving skills and eagerness to learn.
- Experience with deep learning, NLP, or computer vision projects.
- Familiarity with SQL and working with relational or NoSQL databases.
- Exposure to cloud platforms such as AWS, Azure, or GCP.
- Understanding of MLOps concepts and model deployment pipelines.
- Experience with data visualization tools like Tableau or Power BI.
- Participation in hackathons, Kaggle competitions, or open-source projects.
- Work with a high-performing engineering team building intelligent, data-driven SaaS products.
- Flat and collaborative culture where your ideas matter.
- Opportunity to work on real-world AI/ML problems and deploy models in production.
- Exposure to modern ML tools, frameworks, and cloud technologies.
- Access to mentorship, training, and resources to accelerate your AI/ML career growth.
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