Stylz

About the role&nbsp;
We're looking for an AI Intern to work alongside our engineering team on production machine learning systems, not side projects or sandbox demos. You'll ship code that real users touch: training and fine-tuning models, wrapping them in APIs, and helping take them from notebook to deployment.&nbsp;
This is a 4-month structured internship with a clear path to a full-time offer. Interns who&nbsp;meet&nbsp;the performance bar at the end of the program are converted to full-time AI/ML Engineer roles.&nbsp;
What&nbsp;you'll&nbsp;do&nbsp;
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Build and&nbsp;maintain&nbsp;Python services and REST APIs using&nbsp;FastAPI&nbsp;to serve ML models in production&nbsp;
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Work on computer vision problems,&nbsp; image classification, object detection, segmentation, using PyTorch/TensorFlow and OpenCV&nbsp;
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Work on&nbsp;NLP&nbsp;tasks such as text classification, named entity recognition, summarization, and semantic search&nbsp;
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Prototype and evaluate&nbsp;LLM-powered features: prompt engineering, RAG pipelines, embeddings and vector search, function calling, and fine-tuning where appropriate&nbsp;
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Preprocess, clean, and annotate datasets; build reproducible data pipelines&nbsp;
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Run experiments, track results, and clearly document what worked and what&nbsp;didn't&nbsp;
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Benchmark model latency, cost, and accuracy, and help&nbsp;optimize&nbsp;on all three&nbsp;
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Collaborate with engineering and product on scoping, code reviews, and deployment&nbsp;
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What&nbsp;we're&nbsp;looking for&nbsp;
Required&nbsp;
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Strong Python fundamentals, clean, readable, well-structured code&nbsp;
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Hands-on experience with&nbsp;FastAPI&nbsp;(or Flask/Django with a willingness to pick up&nbsp;FastAPI&nbsp;quickly)&nbsp;
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Practical exposure to&nbsp;computer vision&nbsp;and&nbsp;NLP, through coursework, personal projects, research, or a prior internship&nbsp;
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Working knowledge of at least one deep learning framework —&nbsp;PyTorch&nbsp;or&nbsp;TensorFlow&nbsp;
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Familiarity with&nbsp;LLMs&nbsp;and the modern tooling around them (OpenAI/Anthropic/open-source models,&nbsp;LangChain&nbsp;or&nbsp;LlamaIndex, vector databases)&nbsp;
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Comfort with&nbsp;Git, and with reading and debugging code you&nbsp;didn't&nbsp;write&nbsp;
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Solid grasp of ML basics: train/test splits, overfitting, evaluation metrics, and when a model is&nbsp;actually working&nbsp;
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Currently pursuing or recently completed a&nbsp;Degree&nbsp;or&nbsp;Master Degree&nbsp;in CS, Data Science, Engineering, Mathematics, or a related field — or equivalent self-taught experience with a portfolio to show for it&nbsp;
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Nice to have&nbsp;
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Docker and basic cloud experience (AWS, GCP, or Azure)&nbsp;
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Experience deploying a model to production, at any scale&nbsp;
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Familiarity with&nbsp;Hugging Face Transformers&nbsp;
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Exposure to MLOps tooling, MLflow, Weights &amp; Biases, DVC&nbsp;
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SQL and general data-handling comfort&nbsp;
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Open-source contributions, Kaggle results, or published research&nbsp;
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