Machine Learning Engineer

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.

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.