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Remote mid-level ML role increases applicant density despite smaller company brand.
Specialized ML, deep learning, and LLM skills create strong domain bias and limited cross-industry fit.
Explicit 3–5 years plus mandatory production Python and ML stack increases shortlisting rigidity.
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Design and implement machine learning systems for business use cases, focusing on applied ML research and generative AI.
Mentor junior and mid-level data scientists to enhance team capabilities.
Develop and maintain production-grade code leveraging object-oriented Python and advanced ML/DL frameworks.
3-5 years of relevant experience as a Data Scientist or in AI/ML engineering.
Proficiency in Python with strong object-oriented programming skills.
Experience with ML and DL libraries such as Scikit-learn, Keras, TensorFlow, and PyTorch.
In-depth understanding of ML algorithms (supervised/unsupervised) and statistics/probability.
Experienced in applied ML research with ability to read and implement cutting-edge research papers, particularly in generative AI.
Skilled in building and deploying production-level machine learning solutions with mentorship experience.
Familiarity with advanced concepts such as Transformer architectures, large language models, and frameworks like LangChain or LlamaIndex is advantageous but not mandatory.