GQ-626] - Cloud Data Engineer
- Providencia, Santiago
- Permanente
- Tiempo completo
- Productionize machine learning models by building performant data transformations, storage, and pipelines.
- Demonstrate effective, respectful, and honest communication when collaborating with colleagues including a cross-functional team consisting of QA, Operations, and other team members.
- Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment.
- Deliver on company initiatives and projects prioritized for your team and support long term technical vision.**What experience you need**
- BS degree in a STEM major or equivalent job experience required; Master’s Degree preferred; AI/ML coursework preferred
- 2-5 years of related experience
- Experience with ML models design, development or deployment
- Experience with cloud platforms and distributed computing frameworks
- Cloud Certification Strongly Preferred
- 2+ years experience working with software design and **Java, Python and Javascript programming languages**
- 2+ years experience with software build management tools like **Maven or Gradle**
- 2+ years experience with HTML, CSS and frontend/web development
- 2+ years experience with software testing, performance, and quality engineering techniques and strategies
- 2+ years experience with Cloud technology: GCP, AWS, or Azure
- Architect & Develop end-to-end data pipelines (batch & streaming) on GCP using **Dataflow, Pub/Sub, Dataproc, BigQuery, and Cloud Storage**
- Integrate AI/ML models into production pipelines and microservices, collaborating with ML teams to deploy **Tensorflow/PyTorch models via Vertex AI or AI Platform****What could set you apart**An ability to demonstrate successful performance utilizing our Success Profile skills, including:- _Application Development/Programming _- Proficiency in Python; Experience with data processing and machine learning libraries (pandas, numpy, scipy, sklearn, tensorflow, pytorch, etc.)
- _Artificial Intelligence _- Understand how to deploy machine learning models to production systems, including containerization (Docker) and cloud-based deployment
- _Big Data Analytics _- Understand big data analysis, including data cleaning, data transformation and data visualization
- _Cloud Computing _- Understand big data processing frameworks and various database technologies
- _Collaboration _- Excellent verbal and written communication skills to document and present findings clearly
- _Mathematics _- Understand advanced statistical concepts and machine learning algorithms (Logistic Regression, Xgboost, Neural Networks, etc.)
- _Technical Leadership _- Demonstrates an ability to provide guidance to colleagues
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