AI Intern – Entry-Level Artificial Intelligence Internship Opportunity

A mid-sized, innovation-driven tech company builds intelligent solutions for finance, healthcare, and retail clients. The culture values mentorship, rapid learning, and inclusive collaboration.  

Your Mission  

Dive into real production pipelines and sharpen your Artificial Intelligence skills while contributing tangible value. You will gain hands-on practice with state-of-the-art frameworks under senior guidance, accelerating your path toward a full-time AI Engineer role.  

Key Responsibilities  

- Clean, preprocess, and transform large, structured and unstructured datasets for model readiness.  

- Write concise Python scripts that automate data ingestion, feature extraction, and report generation.  

- Execute unit and integration tests on TensorFlow or PyTorch models to verify performance and stability.  

- Document research workflows—assumptions, parameters, metrics—so future iterations remain reproducible.  

- Debug basic issues by tracing logs, inspecting tensors, and comparing model outputs against baselines.  

- Maintain experiment logs in MLflow (or similar) to track hyperparameters and results consistently.  

- Collaborate with Data Scientists and MLOps Engineers during code reviews, sprint planning, and retrospective sessions.  

- Conduct literature reviews on emerging architectures and summarize findings for team discussion.  

- Present weekly learnings to stakeholders, highlighting model health, data quality risks, and recommended fixes.  

Required Qualifications  

- Bachelor’s degree (or final-year standing) in Computer Science, Data Science, Statistics, or related field.  

- Solid foundation in Python, including NumPy, pandas, and virtual environments.  

- Understanding of supervised and unsupervised learning principles, loss functions, and overfitting.  

- Familiarity with TensorFlow, PyTorch, or JAX; able to build and train simple neural networks.  

- Experience cleaning data with SQL and Python—handling nulls, outliers, and encoding categorical variables.  

- Comfortable using Git for version control and collaborative workflows.  

- Analytical mindset, strong problem-solving abilities, and meticulous attention to detail.  

- Clear written and verbal communication that translates complex findings into plain language.  

- Adaptable attitude—you thrive in fast pivots and ambiguous research goals.  

Preferred Extras  

- Exposure to cloud platforms (AWS SageMaker, Google Vertex AI, or Azure ML).  

- Knowledge of RESTful APIs and basic containerization with Docker.  

- Participation in Kaggle competitions, hackathons, or open-source ML projects.  

What You Gain  

- Daily mentorship from senior AI engineers who review your pull requests and demystify model internals.  

- Access to GPU clusters and curated datasets, enabling you to experiment without hardware constraints.  

- Opportunity to author internal blog posts or white papers that bolster your professional portfolio.  

- Flexible remote arrangement within US time zones, fostering work-life balance while maintaining team synergy.