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Mid-level experience band but specialized multimodal ML requirements, yielding moderate competition.
Highly technical multimodal ML/DL requirements, moderately transferable across industries with ML teams.
Multiple mandatory ML, DL, big-data, and cloud requirements plus explicit 4–6 years, so strict filters.
Define and implement mathematically rigorous models to solve complex business challenges.
Develop production-grade, scalable code and data pipelines for large datasets across multiple modalities (text, audio, vision).
Deploy advanced neural network models on cloud platforms, ensuring robust and scalable inference.
4 to 6 years of industry experience as Data Scientist or Machine Learning Engineer.
Proficiency in Python with strong Object-Oriented Programming and design patterns.
Experience with ML libraries (scikit-learn), deep learning frameworks (PyTorch or TensorFlow), and big data tools (Apache Spark, Hadoop).
Bachelor’s or higher degree in quantitative disciplines (Mathematics, Statistics, CS, Physics, etc.).
Expert in translating ambiguous business problems into math-based modeling objectives with optimization focus.
Experienced in end-to-end deployment including cloud architecture and scalable production pipelines.
Demonstrated depth in multimodal deep learning including NLP (Transformers, BERT, GPT), Computer Vision (CNNs, ViTs), and document AI (OCR).