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Strong employer brand and metro location increase applicant competition for visible ML roles.
Specialized ML, CV, embedded AI and MLOps requirements favour candidates with domain-specific backgrounds.
Explicit 7–10 years, PhD/Masters preference and broad ML/MLOps/CV mandates raise screening strictness.
Design, develop, and deploy scalable, production-grade AI/ML solutions emphasizing classical machine learning models and optimization.
Apply deep learning, computer vision, and image processing techniques to build intelligent solutions with clear domain understanding.
Own the end-to-end model lifecycle including feature engineering, evaluation, tuning, deployment, monitoring, and maintenance of AI/ML systems.
7 to 10 years of work experience in machine learning and AI model development and deployment.
Strong proficiency in classical machine learning core algorithms, feature engineering, and model optimization.
Hands-on experience with production-grade AI/ML deployment including cloud platforms and MLOps practices.
Master's or PhD degree in Computer Science, AI, Computer Vision, or related field.
Demonstrates strong expertise in traditional machine learning with practical problem-solving skills grounded in analytics.
Has solid foundational knowledge in deep learning, computer vision, and image processing, and can differentiate and apply each effectively.
Experienced in software engineering best practices to deliver reliable, maintainable AI/ML solutions at scale with production readiness focus.