Machine Learning Engineer

Posted about 24 hours agoViewed

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📍 Location: Argentina, United States, Brazil, Colombia, Spain, Uruguay

🔍 Industry: Data products and Machine Learning Development

🗣️ Languages: English, Spanish

🪄 Skills: AWSPythonSQLApache AirflowETLKafkaKerasKubernetesMachine LearningMLFlowPyTorchSparkTensorflowCI/CD

Requirements:

Proven Expertise: Demonstrated work experience in roles like Machine Learning Engineer, ML Architect, or Cloud Engineer.
AI/ML Proficiency: Deep understanding of AI/ML principles including neural networks and various ML models.
Data Architecture Knowledge: Familiarity with Data Warehouses, Data Lakes, and DevOps methodologies.
ETL and ML Workflow Experience: Participation in data processing ETL and ML workflows.
Deep Learning Competence: Proficiency in frameworks such as Keras, PyTorch, TensorFlow.
Programming Skills: Strong proficiency in Python and at least one other strongly typed language.
Mathematical and Statistical Acumen: Knowledge in mathematical modeling and statistical principles.
MLOps Mastery: Experience in Machine Learning systems, including model lifecycle management.
Strategic Thinking: Ability to develop implementation plans.
Exceptional capacity for teamwork.
English Intermediate Level and Spanish Advanced Level.

Responsibilities:

Lead ML Model Productization: Champion the productization of ML models following MLops best practices.
ML POC Development: Collaborate with data scientists to develop meaningful ML Proofs of Concept for internal and client requirements.
ML Model Lifecycle Management: Oversee and optimize the lifecycle of Machine Learning models for performance and efficiency.
Business-Technical Translation: Convert business and mathematical/statistical requirements into software implementations.
Research and Innovation: Investigate new ML technologies to enhance business value.
Bridge DS and DE Roles: Act as a liaison between Data Science and Data Engineering roles.
Project Strategy: Assist in defining project roadmaps and timelines.
Knowledge Sharing: Document and promote industry best practices in AI/ML.
Technical Interviews: Support interview processes for technical recruitment.

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