Machine Learning Engineer II

Expedia Expedia · Hospitality · Gurgaon, India

Machine Learning Engineer II at Expedia Group focused on building and integrating generative AI solutions, including RAG and AI agents, into existing workflow systems. The role involves designing and coding large-scale data pipelines, managing the end-to-end ML model lifecycle, and deploying batch and real-time inferencing applications using big data technologies like Spark and Databricks.

What you'd actually do

  1. Work in a cross-functional geographically distributed team of Machine Learning engineers and ML Scientists to design and code large scale batch and a few real-time data pipelines on the Cloud.
  2. Prototype creative solutions quickly by developing minimum viable products and work with seniors and peers in crafting and implementing the technical vision of the team
  3. Actively participate in all phases of the end-to-end ML model lifecycle (includes feature engineering, model training, model scoring, model validation) for enterprise applications projects to tackle sophisticated business problems in production environments
  4. Collaborate with global team of data scientists, administrators, data analysts, data engineers, and data architects on production systems and applications
  5. Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems.

Skills

Required

  • Spark
  • Hive
  • Databricks
  • Python
  • Batch and Real-Time Inferencing
  • prompt engineering
  • Retrieval-Augmented Generation (RAG)
  • AI agents
  • LLM orchestration frameworks

Nice to have

  • Scala
  • OOAD
  • design patterns
  • SQL
  • NoSQL
  • machine learning pipelines
  • ML Lifecycle
  • GenAI solutions integration
  • enterprise systems
  • APIs
  • data platforms
  • AWS
  • Airflow
  • traditional Machine Learning algorithms
  • Generative-AI algorithms

What the JD emphasized

  • Must have experience in big data technologies, particularly Spark, Hive, and Databricks.
  • Proficiency in Python and experience developing and deploying Batch and Real-Time Inferencing applications.
  • Good understanding of prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and LLM orchestration frameworks.

Other signals

  • design and code large scale batch and a few real-time data pipelines on the Cloud
  • end-to-end ML model lifecycle (includes feature engineering, model training, model scoring, model validation)
  • integrate generative AI solutions into existing workflow systems
  • big data technologies, particularly Spark, Hive, and Databricks
  • Python and experience developing and deploying Batch and Real-Time Inferencing applications
  • prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and LLM orchestration frameworks