Machine Learning Engineer

Cuesta Partners is looking for a Machine Learning Engineer in Latin America interested in consulting, to work with our broad base of clients on various engagements, delivering AI-driven solutions using modern technologies and best practices while mentoring others.

As a member of our data practice, you will play a key role in shaping our clients' AI and machine learning capabilities. Your mission will be to develop and deploy machine learning models that enhance business insights and operational efficiencies. You will collaborate with cross-functional teams to ensure seamless model integration and optimization.

What We're Looking For:

• 3-5 years of prior experience as a ML Engineer.

• Bachelor's or Master’s degree in Computer Science, AI, Data Science, or related field.

• Proven experience as a Machine Learning Engineer with expertise in designing, developing, and deploying ML models.

• Proficiency in Python and ML libraries such as TensorFlow, PyTorch, and scikit-learn.

• Strong understanding of algorithms, statistics, and machine learning techniques.

• Experience deploying models using cloud platforms (AWS, GCP, or Azure).

• Strong problem-solving skills and ability to work in a collaborative environment.

What You'll Do:

• Design, develop, and deploy machine learning models for a variety of applications, including predictive analytics, classification, and clustering.

• Build and optimize scalable, end-to-end ML pipelines from data preprocessing and feature engineering to deployment and retraining, ensuring efficient data flow and robust performance.

• Collaborate with data scientists, data engineers, and MLOps teams to ensure clean datasets, scalable infrastructure, and adherence to best practices in model deployment.

• Integrate ML models into production environments and continuously monitor, evaluate, and retrain them to maintain effectiveness and accuracy.

• Implement cloud-based solutions to support scalable and efficient machine learning workflows.

• Stay up to date on emerging ML/AI technologies and recommend new tools and techniques to enhance model performance and outcomes.

• Document processes and develop technical materials to support model transparency, reproducibility, and interpretability.