Machine Learning Engineer
M
Mutt DataData products and Machine Learning Development
Argentina, United States, Brazil, Colombia, Spain, Uruguay
Salary not disclosed
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Job Details
- Languages
- English, Spanish
- Required 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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