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Tier-1 brand, mid-level ML role, hybrid location, and broad ML skillset increase competition.
Core ML engineering skills are transferable, but personalization and recommender expertise increases domain specificity.
Explicit 4+ years, production ML experience, and required frameworks enforce moderate candidate filtering.
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Design, build, and deploy scalable machine learning systems impacting personalization, search, and recommendation across Autodesk's go-to-market ecosystem.
Own end-to-end ML initiatives from problem framing, modeling, experimentation, to production deployment and monitoring.
Collaborate closely with product, engineering, analytics, and research teams and contribute to ML architecture, best practices, and mentoring in the Bangalore team.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related quantitative field.
4+ years industry experience building and deploying machine learning systems in production.
Strong hands-on experience across end-to-end ML lifecycle including data preparation, modeling, evaluation, deployment, and monitoring.
Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). Experience with large-scale structured/unstructured datasets and software engineering best practices.
Experienced in production-grade ML systems with end-to-end ownership and strong operational impact focus.
Familiarity with personalization, recommendation systems, search or ranking systems preferred.
Comfortable working in a hybrid model and collaborating with global cross-functional teams.