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Job Title: Machine Learning Engineer (Data & AI)
Location: Remote
Time Zone: US Time Zones (EST–PST)

Role Overview
We are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The idóneo candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines.

Key Responsibilities
Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering
Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering,



and NLP tasks
Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets
Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines
Work with structured and unstructured data at scale — including text, tabular, and time-series data
Monitor model performance in production and implement retraining and drift-detection strategies
Collaborate with engineering and product teams to translate data insights into actionable AI features
Document data schemas, model architectures, and pipeline logic clearly and thoroughly

Required Qualifications
Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar)
Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets
Practical experience with model training, evaluation, hyperparameter tuning

📌 Machine Learning Engineer (Piedras)
🏢 Hire Hangar
📍 Piedras