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Tier-1 brand, mid-level generalist ML role, and metro location increase applicant competition.
ML engineering skills are transferable, but enterprise/edge product experience increases domain specificity.
Explicit 2–4 years requirement plus ML skillset and degree makes screening moderately strict.
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Design, develop, and implement machine learning models and algorithms to solve specific business problems, including research and experimentation.
Prepare and preprocess large datasets for ML tasks, ensuring quality data through cleaning, normalization, and feature extraction.
Collaborate with cross-functional teams to align on requirements, present results, contribute to design reviews, and communicate findings effectively to stakeholders.
Bachelor's degree in computer science, engineering, data science, machine learning, AI, or closely related quantitative field; Master's degree desirable.
2-4 years of professional experience in AI/ML engineering roles or relevant projects.
Proficiency in programming languages such as Python, R, or Java with experience in ML libraries/frameworks like TensorFlow, PyTorch, scikit-learn, or Keras.
Hands-on experience in data preprocessing, model development (linear regression, decision trees, random forests, SVM, deep learning), and model evaluation including hyperparameter tuning and cross-validation.
Intermediate-level AI/ML engineer comfortable working with real-world datasets and addressing data quality issues.
Capable of independent work within an established framework with moderate supervision and providing technical recommendations.
Experience collaborating across teams including data scientists and software engineers, with strong skills in communicating complex ML concepts to non-technical stakeholders.