Back
Tier-1 brand, mid-level generalist ML title, and broad GenAI/ML skillset drive high applicant competition.
Machine learning skills and frameworks are broadly transferable across industries, lowering background sensitivity.
Explicit 2-5 year requirement plus mandatory ML frameworks, production deployment, and domain expertise increases filter rigidity.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy machine learning models spanning classical ML, deep learning, and Generative AI for diverse business use cases.
Engineer high-impact features from raw data and optimize model performance using techniques like SHAP for interpretability.
Build end-to-end ML pipelines and collaborate to deploy models into production environments.
Bachelor's Degree preferred but combinations of coursework and experience considered.
2-5 years of relevant work experience in Machine Learning or related fields.
Strong proficiency in Python programming with experience in ML frameworks like PyTorch or TensorFlow.
Experience applying classical ML algorithms and emerging Generative AI technologies to real-world problems.
Experienced ML engineer familiar with both classical ML algorithms and deep learning architectures including Transformers, BERT, GPT-style models.
Skilled in feature engineering, model interpretability, and experimental comparison of model performance.
Capable of developing production-ready, modular, and well-documented code and ML pipelines in collaborative environments.