Responsibilities

• Input to ML pipeline design, development, and operating lifecycle following best practices
• Designing, creating, maintaining, resolving issues, and enhancing ML pipeline steps
• Ownership and contribution to ML prediction endpoints' design and implementation
• Collaboration with System Engineers to set up ML lifecycle management environments
• Specification, documentation, and user guide writing for developed applications
• Promoting the improvement of coding practices and the organization of repositories in the science work cycle
• Establishment and configuration of pipelines for projects
• Continuous identification of technical risks and gaps, with devising of mitigation strategies
• Collaboration with data scientists to transition predictive models to production, understanding the scope and purpose of the models, and creating scalable data preparation pipelines

Requirements

• 3+ years of experience with programming languages, preferably Python, and strong SQL knowledge
• Robust MLOps experience (Sagemaker, Vertex, or Azure ML)
• Intermediate level in Data Science, Data Engineering, and DevOps Engineering
• Experience with at least one project delivered to production in an MLE role
• Expert level in Engineering Best Practices
• Practical experience implementing Data Products using the Apache Spark Ecosystem (Spark SQL, MLlib/SparkML) or similar technologies
• Familiarity with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.)
• Experience with automated data pipeline and workflow management tools, i.e., Airflow, Argo Workflow, etc
• Experience with different data processing paradigms (batch, micro-batch, streaming)
• Practical experience working with at least one of the major Cloud Providers such as AWS, GCP, and Azure
• Production experience integrating ML models into complicated data-driven systems
• DS experience with Tensorflow/PyTorch/XGBoost, NumPy, SciPy, Scikit-learn, Pandas, Keras, Spacy, HuggingFace, Transformers
• Experience with different types of databases (Relational, NoSQL, Graph, Document, Columnar, Time Series, etc.)

Nice to have

• Practical experience with Databricks MLOps-related tools/technologies like MLFlow, Kubeflow, TensorFlow Extended (TFX)
• Experience with performance testing tools like JMeter or LoadRunner
• Knowledge of containerization technologies like Docker

We offer

• Improved medical coverage - EPAMers are eligible to participate in a supplementary health insurance program that shall have the usual coverage in the industry, with the Company funding 100% of the value of the monthly premium for participation
• Lunch Allowance - You will receive a daily allowance of CLP $ 7.000 per working day. Enjoy a nice meal on us
• Allowance for internet and electricity - You will receive an allowance of CLP$15.000 per month to cover internet and electricity expense
• National Holiday Bonus - We celebrate joining the Chilean Market. That is why all our employees will receive a bonus of CLP $86,646 in September
• Christmas Bonus - You will receive an End of Year bonus of CLP $170,539. It will be paid during the month of December, to ensure you have a Happy Holiday!
• Learning Culture - We want you to be the best version of yourself, that is why we offer unlimited access to learning platforms, a wide range of internal courses, and all the knowledge you need to grow professionally
• Additional Income - Besides your regular salary, you will also have the chance to earn extra income by referring talent, being a technical interviewer, and many more ways
• Are you open to relocation? - If you want to relocate to another country and we have the right project, we will assist you every step of the way, to help you and your family, reach your new home