Senior ML Engineer

Autodesk Autodesk · Enterprise · Bangalore, India

Senior ML Engineer at Autodesk's Growth and Experience Technology (GET) organization, focusing on building and deploying production-grade ML systems for personalization, recommendations, search, and generative AI across the customer journey. The role involves the full ML lifecycle, from problem framing to deployment, with a strong emphasis on scalable infrastructure and product partnership.

What you'd actually do

  1. Design, build, and deploy scalable machine learning systems across personalization, search, recommendations, and retrieval use cases
  2. Develop and evaluate models using robust experimentation frameworks and data-driven methodologies
  3. Own components of end-to-end ML pipelines, including feature engineering, model training, validation, deployment, and monitoring
  4. Write production-quality code and contribute to scalable, maintainable ML infrastructure
  5. Analyze large datasets to extract insights and translate business problems into well-defined ML tasks

Skills

Required

  • 4+ years of industry experience building and deploying machine learning systems in production environments
  • Strong hands-on experience across the end-to-end ML lifecycle (data preparation, modeling, evaluation, deployment, monitoring)
  • Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn)
  • Experience working with structured and/or unstructured large-scale datasets
  • Solid understanding of software engineering best practices (version control, testing, CI/CD)
  • Collaborative mindset and ability to work effectively in a global team

Nice to have

  • MS or PhD in a relevant field
  • Experience with personalization, recommendation systems, search, or ranking systems
  • Experience with Generative AI, LLM fine-tuning, prompt engineering, or RAG systems
  • Experience building or operating ML systems at scale (cloud platforms such as AWS, GCP, or Azure)
  • Experience with model deployment frameworks or MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI)
  • Familiarity with experimentation platforms (A/B testing, online metrics evaluation)
  • Ability to communicate technical concepts clearly to cross-functional stakeholders

What the JD emphasized

  • production environments
  • end-to-end ML lifecycle
  • production-quality code
  • scalable, maintainable ML infrastructure
  • large-scale datasets
  • Generative AI, LLM fine-tuning, prompt engineering, or RAG systems
  • ML systems at scale

Other signals

  • production-grade ML systems
  • personalization, recommendations, search
  • generative AI capabilities