Build and experiment with ML models and LLM-powered systems (classification, embeddings, retrieval, agents).
Work on real-world use cases such as retail personalization, workflow automation, and customer intelligence.
Design and improve prompt pipelines, evaluation frameworks, and agent behaviors.
Collaborate on data pipelines: ingestion, cleaning, feature engineering, and analysis.
Prototype and ship features quickly in a production environment.
Contribute to internal tooling for model monitoring, evaluation, and iteration.

Strong fundamentals in machine learning and statistics.
Hands-on experience with Python and ML libraries (e.g., PyTorch, scikit-learn).
Familiarity with LLMs, embeddings, vector databases, or agent frameworks is a strong plus.
Comfort working with APIs and integrating ML into applications.
Ability to move fast, experiment, and learn independently.
Clear thinking and ability to translate messy problems into structured solutions.
Bonus points
Experience building AI agents or working with tools like LangChain, OpenAI APIs, etc.
Exposure to retail, ecommerce, or customer data problems.
Experience with data engineering (SQL, pipelines, ETL).
Side projects or demos showcasing applied ML work.

GoodlightAI

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