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Mid-level ML engineer, broad skillset, and generalist requirements increase applicant competition.
ML engineering skills are moderately transferable across industries but require domain-specific data familiarity.
Explicit 2–5 years requirement and mandatory ML/domain skills create moderate filtering.
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Develop, test, deploy, and maintain machine learning models for web and mobile applications.
Prepare and analyze datasets including text and document data, and improve model performance and reliability.
Create APIs for ML services and integrate models with backend systems, monitoring performance post-deployment.
2 to 5 years of practical Machine Learning experience.
Strong Python skills including NumPy, Pandas, Scikit-learn, SQL, REST APIs, Git, and basic Linux knowledge.
Ability to clean, preprocess data, evaluate models, and work with machine learning algorithms.
Location: Lucknow, Uttar Pradesh; Full-time position.
Experienced in end-to-end ML lifecycle: data preparation, model development, testing, API integration, and production deployment.
Comfortable working in cross-functional teams including backend and data engineering.
Familiarity with production ML systems and improving model performance in real business applications.