Argentina onlyWe are seeking an experienced Machine Learning Engineer to join our team. The successful candidate will be responsible for the end-to-end lifecycle of machine learning systems, from designing, training, and deploying ML models for production use cases to developing robust data pipelines for preprocessing and serving. A strong focus will be on implementing MLOps best practices, including CI/CD, monitoring, and model lifecycle management
Responsibilities
- Design, train, and deploy machine learning models for production use cases.
- Develop data pipelines for preprocessing, feature engineering, and model serving.
- Implement MLOps best practices: CI/CD for ML, monitoring, retraining workflows, and model lifecycle management.
- Build and validate ML Proof of Concepts (POCs) for new business opportunities.
- Optimize models for performance, scalability, and efficiency (latency, memory, throughput).
- Collaborate with data engineers, DevOps, and product teams to ensure seamless integration of ML systems.
- Stay up to date with new ML/AI research and tools to bring innovation into projects.
Required Skills
- Proven experience building and deploying ML systems in production.
- Strong proficiency in Python (Pandas, NumPy, Scikit-Learn, XGBoost, PyTorch/TensorFlow, Jupyter).
- Knowledge of SQL, Spark and data manipulation.
- Familiarity with MLOps tools (MLflow, SageMaker, or Vertex AI).
- Hands-on experience with cloud platforms (AWS, GCP, or Azure).
- Strong understanding of software engineering best practices (testing, version control, CI/CD).
- Nice to have: experience with feature stores, time series modeling, or generative AI techniques.
United States
AI, AG + 43 more
