The Machine Learning Engineer is responsible for designing and implementing machine learning models to solve complex problems and drive innovation. This role is crucial in leveraging data to enhance product offerings and improve decision-making processes within the company.
- Develop and deploy machine learning models for various applications.
- Collaborate with data scientists to refine algorithms based on business needs.
- Optimize model performance through parameter tuning and feature engineering.
- Maintain scalable infrastructure for model training and deployment.
- Analyze large datasets to extract meaningful insights.
- Stay updated with the latest advancements in machine learning technologies.
- Proficiency in programming languages such as Python or R.
- Experience with machine learning frameworks like TensorFlow or PyTorch.
- Strong analytical skills with a focus on problem-solving.
- Ability to work collaboratively in a team environment.
The Machine Learning Engineer's performance is evaluated based on successful deployment of models, improvement in prediction accuracy, reduction of processing time, and contribution to innovative solutions that meet business objectives efficiently.
Model Deployment
Timely deployment of accurate ML models into production.
Prediction Accuracy
Enhancement of model accuracy over baseline metrics.
Processing Efficiency
Reduction in computational time for model training
Reports to
Lead Data Scientist
Collaborates with
Data Analysts, Software Engineers
Leads
- TensorFlow
- PyTorch
- Scikit-learn
Bachelor's degree in Computer Science, Engineering, or related field; 2-4 years experience working with machine learning algorithms.
