Full Stack Engineering Intern

About the Role

We are seeking a motivated and enthusiastic Intern - Full Stack AI Engineer to join our AI/ML team. This internship offers hands-on experience in building AI-driven applications, combining machine learning development with software engineering. You will assist in developing and deploying AI models, creating APIs, and building user-facing tools under the guidance of senior engineers.

This is an excellent opportunity for students or early-career professionals to gain practical experience in full stack development and AI/ML technologies.

Key Responsibilities

• Assist in developing and testing machine learning models for production environments.
• Support the creation of APIs and backend services for AI/ML model inference and data processing.
• Contribute to building interactive web applications that integrate AI functionalities.
• Help implement MLOps practices, such as model versioning and monitoring.
• Collaborate with data scientists and engineers to transform prototypes into functional services.
• Participate in optimizing model performance and application scalability.
• Contribute to code reviews, documentation, and maintaining clean, organized codebases.
• Learn and apply software engineering best practices, including version control and CI/CD pipelines.

Required Qualifications

• Basic knowledge of Python and familiarity with libraries like TensorFlow, PyTorch, or scikit-learn.
• Understanding of web development basics, including HTML, CSS, and JavaScript

(React, Vue.js, or Angular is a plus).

• Familiarity with RESTful APIs and frameworks like FastAPI, Flask, or Django.
• Exposure to cloud platforms (AWS, GCP, or Azure) or interest in learning cloud-based AI services.
• Basic understanding of version control (e.g., Git) and software development workflows.
• Strong problem-solving skills and eagerness to learn new technologies.

Preferred Qualifications

• Exposure to real-time systems or tools like Kafka, Redis, or WebSockets.
• Familiarity with data processing tools (e.g., Pandas, NumPy) or ML pipeline tools (e.g. ML flow, Airflow).
• Basic knowledge of large language models (LLMs), NLP, or computer vision.
• Experience with frontend visualization tools (e.g., D3.js, Plotly).
• Awareness of ethical AI practices, model interpretability, or bias mitigation.

Learning Opportunities

• Gain hands-on experience with AI/ML model development and deployment.
• Learn to build scalable, production-grade AI applications.
• Work with cutting-edge technologies in full stack development and AI/ML.
• Receive mentorship from experienced engineers and data scientists.
• Develop skills in MLOps, cloud platforms, and modern software engineering practices.