Machine learning engineer (MLE)

Machine learning engineer (MLE)


Colombia


Job Description
• Work with data scientists and analysts to create and deploy new models and ML
systems.
• Implement end-to-end solutions across the full breadth of ML model development
lifecycle. The specific role includes working hand in hand with the scientists from
the point of data exploration for model development to the point of building
features, ML pipelines and deploying them in production. You will have an
opportunity to work on both batch and real time models. The role also involves
operational support.
• Establish scalable, efficient, automated processes for data analyses, model
development, validation and implementation
• Write efficient and scalable software to ship products in an iterative, continual[1]release environment
• Write optimized data pipelines to support machine learning models
• Contribute to and promote good software engineering practices across the team
and build cloud native software for ML pipelines
• Contribute to and re-use community best practices
• Embody the values and passions that characterize Levi Strauss & Co., with
empathy to engage with colleagues from multiple backgrounds
Example Projects
Besides driving the transformation of Levi's into a data-driven enterprise in general, here
are some specific projects you will work on and contribute to:
• Forecasting models for planning and allocation
• Promotion recommendation tool
• Pricing elasticity modeling


General Requirement:
• University or advanced degree in engineering, computer science, mathematics,
or a related field
• 3+ years' experience(for mid level), 5+ years' experience(for Sr. Level)
developing and deploying machine learning systems into production
• Experience working with big data tools: Spark, Hadoop, Kafka, etc.
• Experience with at least one cloud provider solution (AWS, GCP, Azure) and
understanding of serverless code development. GCP experience preferred.
• Efficiency with object-oriented/object function scripting languages, Python
required.
• Efficiency with Python data-handling libraries like Pandas or Pyspark.
• Efficiency in SQL for data consumption and transformation. Nice to have:
SparkSQL, BigQuery SQL dialects.
• Expertise in standard software engineering methodology, e.g. unit testing, test
automation, continuous integration, continuous deployment, code reviews,
design documentation
• Working experience with native ML orchestration systems such as Kubeflow,
Vertex AI Pipelines, Airflow, TFX...
• Relevant working experience with Docker and Kubernetes is a big plus.


Specific requirement for MLE with data engineering focus:
• Knowledge of data pipeline and workflow management tools
• Expertise in data engineering, analysis and processing (e.g. designing and
maintaining ETLs, validating data and detecting quality issues)


Specific requirement for MLE with data science focus
• Knowledgeable in statistics and machine learning
• Previous experience developing predictive models in a production environment,
MLOps and model integration into larger scale applications