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Tier-1 employer, hybrid remote, mid-level ML role in Bangalore with broad skill requirements and high applicant density.
Core ML engineering skills are transferable, but personalization and MLOps experience moderately increases domain specificity.
Explicit 4+ years requirement plus mandatory end-to-end ML lifecycle and framework proficiency increases strictness.
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Design, build, and deploy scalable machine learning systems for personalization, search, recommendations, and retrieval that impact Autodesk's go-to-market ecosystem.
Own end-to-end ML initiatives from problem framing, experimentation, through production deployment, collaborating with product, engineering, analytics, and research teams.
Contribute to ML architecture decisions, uphold engineering standards, and mentor junior engineers where appropriate.
Bachelor’s or master’s degree in Computer Science, Data Science, Statistics, or related quantitative field.
4+ years of industry experience building and deploying machine learning systems in production.
Strong experience with end-to-end ML lifecycle including feature engineering, model training, validation, deployment, and monitoring.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn; experience with large-scale structured or unstructured datasets.
Experienced in deploying ML systems at scale with sound understanding of software engineering best practices (version control, testing, CI/CD).
Comfortable collaborating cross-functionally with product, design, engineering, and analytics teams to deliver measurable business impact.
Familiarity with personalization, recommendation, search, ranking systems, and emerging generative AI or LLM technologies is advantageous but not mandatory.