Machine Learning Engineer

hace 2 semanas

Desde casa, Colombia Gorilla Logic A tiempo completo

Machine Learning Engineer


Gorilla Logic is looking for a Machine Learning Engineer, you will be part of a fast paced environment and team that is building a ML Platform on Databricks and using many open source technologies (Python, Spark, Kafka, Delta, Bazel, MLFlow).

You will be responsible for building out scalable machine learning infrastructure and pipelines in order to support and operationalize models.


Responsibilities:

  • Design, prototype and build machine learning systems, frameworks, pipelines, libraries, utilities and tools that process data for ML tasks
  • Translate data science prototypes into scalable production implementations
  • Partner with data scientists to troubleshoot and optimize complex data pipelines
  • Build ML Platform that can simplify implementing new models
  • Build endtoend reusable pipelines from data acquisition to model output delivery
  • Design, develop, deploy, and maintain productiongrade scalable data transformation, machine learning, time series models and deep learning code, pipelines, and dashboards; manage data and model versioning, training, tuning, serving, experiment and evaluation tracking implementations
  • Perform code reviews to ensure architecture, code, and data standards are followed

Technical Requirements

  • 3+ years of solid handson Machine Learning Engineering experience with focus on MLOps
  • Proven experience in building and deploying machine learning models using Python libraries like MLFlow, MLRun, scikitlearn, PyTorch, MLLib


 Programming Languages
  • Python (PySpark), SQL; exposure to other languages (Scala, Java, C#, JavaScript).
  • Thorough understanding of programming fundamentals such as OOP, data structures, and algorithm design.
  • Experience with distributed compute engines (Apache Spark), cloudbased MPP databases (Snowflake, Bigquery, Redshift), and Data Lakes (Azure Data Lake, S3).
  • Expertise in building software and systems that scale through a focus on MLOps
  • Experience integrating Machine Learning models in production (batch, streaming and
online)

  • Fluent in Machine Learning algorithms
  • Experience in writing data pipeline and machine learning libraries and utilities
  • Industry experience building and productionizing innovative endtoend Machine
Learning systems

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