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Tier-1 employer, hybrid role, Bangalore metro, popular ML title and mid-level experience make competition high.
Core ML engineering skills are transferable broadly, but personalization and recommendation focus adds moderate domain specificity.
Explicit 4+ years, mandatory end-to-end ML experience and required tech stack create high shortlisting strictness.
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Design, build, and deploy scalable machine learning systems focused on personalization, search, recommendations, and retrieval across Autodesk’s customer journey.
Own end-to-end ML pipelines including feature engineering, model training, validation, deployment, and monitoring with measurable business impact.
Collaborate closely with product, engineering, analytics, and research teams while contributing to architectural decisions, code reviews, and mentoring junior engineers.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related quantitative field.
4+ years of industry experience building and deploying ML systems in production environments.
Proficiency in Python and ML frameworks like PyTorch, TensorFlow, or Scikit-learn.
Experience with end-to-end ML lifecycle and handling large-scale structured or unstructured datasets.
Experienced in personalization, recommendation, search, or ranking ML systems within a production environment.
Skilled at partnering cross-functionally with product, design, engineering, and analytics to deliver measurable outcomes.
Strong software engineering practices including version control, testing, and CI/CD in ML infrastructure contexts.