Sr. Engineer - Data & ML Platform (hybrid, Ind)

CrowdStrike CrowdStrike · Enterprise · Bangalore, India

CrowdStrike is seeking a Sr. Engineer to build out their ML Experimentation Platform from the ground up. This role involves designing, implementing, and maintaining scalable ML pipelines for data preparation, feature engineering, model training, and model serving. The engineer will also contribute to a production-focused culture and future generative AI investments. The role requires strong experience in distributed systems, data platforms, and ML concepts, with a focus on production deployment and CI/CD.

What you'd actually do

  1. Help design, build, and facilitate adoption of a modern Data+ML platform
  2. Modularize complex ML code into standardized and repeatable components
  3. Establish and facilitate adoption of repeatable patterns for model development, deployment, and monitoring
  4. Build a platform that scales to thousands of users and offers self-service capability to build ML experimentation pipelines
  5. Leverage workflow orchestration tools to deploy efficient and scalable execution of complex data and ML pipelines

Skills

Required

  • Python
  • distributed computing
  • Kubernetes
  • Airflow
  • Terraform
  • FluxCD
  • CI/CD
  • containerization
  • Apache Spark
  • Flink
  • ML Platform tools (Jupyter Notebooks, NVidia Workbench, MLFlow, Ray, Vertex AI)
  • supervised / unsupervised approaches

Nice to have

  • Java
  • Scala
  • Go
  • Iceberg
  • Pinot
  • Jenkins
  • Parquet
  • Protocol Buffers
  • GRPC

What the JD emphasized

  • 3+ years experience developing and deploying machine learning solutions to production
  • 3+ years experience with ML Platform tools
  • Experience building data platform product(s) or features
  • Expert level experience with Python
  • Expert level experience with CI/CD frameworks
  • Expert level experience with containerization frameworks
  • Distributed Systems Knowledge
  • Data Platform Experience
  • Machine Learning concepts

Other signals

  • ML Experimentation Platform from the ground up
  • Data Preparation, Cataloging, Feature Engineering, Model Training, and Model Serving
  • production-focused culture that bridges the gap between model development and operational success
  • generative AI investments