Job Description
AI/ML Engineer Intern About the Role We're looking for a motivated AI/ML intern who's eager to learn, move fast, and build real production systems. This isn't a coffee-fetching internship - you'll work on meaningful projects that ship to production, collaborate with experienced engineers, and gain hands-on experience across the ML lifecycle. You'll be exposed to greenfield projects, modern ML infrastructure, and real-world problem-solving. We're looking for someone who's curious, scrappy, and comfortable figuring things out independently with guidance from the team. What You'll Do Build and deploy real ML models (not just Jupyter notebooks) Contribute to production ML pipelines and infrastructure Work with data - cleaning, processing, feature engineering Learn MLOps practices - Docker, CI/CD, model deployment Collaborate with senior engineers on research and implementation Ship code to production (with supervision and code review) Experiment with new ML techniques and tools Required Skills Core (Must Have) Python - Strong programming skills, comfortable writing clean code beyond notebooks ML Fundamentals - Solid understanding of machine learning concepts through coursework or personal projects Supervised/unsupervised learning Model evaluation and validation Basic understanding of neural networks Data Manipulation - Experience with Pandas, NumPy, or similar libraries Version Control - Git basics (commit, push, pull, branches) Problem Solving - Can debug issues and search for solutions independently Communication - Can explain technical concepts clearly At Least One of These ML Areas NLP/LLMs : Experience with transformers, text processing, or language models (even if just through projects) Time Series : Forecasting, sequential data, or time-based analysis Recommender Systems : Collaborative filtering or ranking (through projects/coursework) Computer Vision : Image classification, object detection, or CNN projects Classical ML : Strong foundation in scikit-learn, feature engineering, model selection Working Style Self-starter - You don't wait to be told what to do; you ask questions and take initiative Fast learner - Comfortable picking up new tools and technologies quickly CLI-comfortable - Not afraid of the terminal (we can teach you more advanced usage) Curious - Genuinely interested in how things work under the hood Collaborative - Can work with a team and ask for help when stuck Nice to Have Cloud Experience : Any exposure to AWS, Azure, GCP, or OCI Docker/Containers : Basic understanding of containerization SQL : Database querying experience CI/CD : Familiarity with GitHub Actions, Azure DevOps, or Jenkins MLOps Tools : Exposure to MLflow, Weights & Biases, or Airflow Bash/PowerShell : Basic scripting skills Deep Learning Frameworks : PyTorch or TensorFlow experience Kaggle Competitions : Participated in ML competitions Open Source : Contributions to projects or active GitHub profile Additional Languages : Any experience with Go, Rust, or JavaScript What We'll Teach You Production ML deployment and monitoring MLOps best practices and tools Cloud-native AI/ML services across multiple providers Building scalable data pipelines Advanced CLI workflows and productivity tools Code review and software engineering practices Working in a professional engineering environment Our Stack: Core : Python | PyTorch/TensorFlow | Scikit-learn | FastAPI/Flask | Git | Bash/PowerShell ML/AI Tools : MLflow | Airflow/Kubeflow | Azure AI | AWS SageMaker/Bedrock | GCP Vertex AI | OCI AI Services Infrastructure : Docker | Kubernetes | AWS/Azure/GCP/OCI | PostgreSQL | Azure DevOps | GitHub Actions Education & Experience Currently pursuing Bachelor's or Master's in Computer Science, Data Science, Machine Learning, or related field Minimum GPA of 3.0 preferred <