Overview
Machine Learning Engineer to design, develop, deploy, and support scalable machine learning solutions, including predictive models and Generative AI/LLM applications.
What you'll do
- Design, train, evaluate, and deploy predictive machine learning models for business use cases.
- Build reusable ML pipelines across ingestion, feature engineering, training, validation, deployment, monitoring, and retraining.
- Develop forecasting solutions using historical data, time-series features, backtesting, and validation.
- Operationalize models with MLOps practices such as experiment tracking and CI/CD.
- Create production APIs and services to expose ML capabilities to applications.
- Implement monitoring and alerting for accuracy, forecast performance, data quality, drift, bias, and latency.
- Develop LLM solutions using embeddings, vector search, prompt engineering, evaluation, and monitoring.
What you'll need
- 5–8 years of experience in software engineering, data science, machine learning engineering, computer science, information technology, or a related field.
- Strong hands-on experience with Python and SQL.
- Experience with ML libraries including Scikit-learn, PyTorch, TensorFlow, or XGBoost (or equivalents).
- Skills in preprocessing, feature engineering, model selection, training, hyperparameter tuning, and evaluation.
- Experience implementing MLOps pipelines for model development, deployment, monitoring, versioning, and retraining.
- Hands-on experience with AWS for production ML development and deployment (AWS strongly preferred).
- Experience building APIs or services for machine learning models.
Nice to have
- Experience developing Generative AI and Large Language Model applications.
- Experience with additional forecasting, NLP, computer vision, recommendation, or optimization models.
- Experience with platforms such as MLflow, Databricks Machine Learning, Azure Machine Learning, or Vertex AI.
- Experience with Kubernetes, infrastructure as code, and cloud-native deployment patterns.
- AWS-native MLOps architectures and services experience.
Details
- Occasional off-hours support is part of production support activities.
Read the full description and apply on the company’s own careers page.
