About the Role
The Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models to solve real-world business challenges. This role focuses on hands-on model development, data preparation, and integration of ML solutions into production systems. The ideal candidate is a strong collaborator who works closely with fellow Machine Learning Engineers, MLOps Engineers, and cross-functional teams to deliver scalable and impactful AI solutions, including generative AI models.
Responsibilities and Duties
- Model Development: Design, build, and deploy machine learning models—including generative AI models—to address complex business problems.
- Data Collaboration: Work with teammates to preprocess, clean, and analyze structured and unstructured data for use in ML pipelines.
- Performance Optimization: Tune and optimize models for accuracy, efficiency, and scalability in production environments.
- Integration Support: Collaborate with MLOps Engineers to ensure smooth deployment, monitoring, and maintenance of ML models.
- Continuous Learning: Stay informed on the latest advancements in machine learning, deep learning, and generative AI technologies, and apply them where appropriate.
- Team Collaboration: Contribute to a collaborative engineering culture by sharing knowledge, participating in code reviews, and supporting team initiatives.
Required Qualifications
- Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).
- 3 years total experience.
- Hands-on experience with ML frameworks such as scikit-learn, TensorFlow, PyTorch, and spaCy.
- Proficiency in Python and familiarity with ML development workflows.
- Strong analytical and problem-solving skills with attention to detail.
- Effective communication and collaboration skills in a team-oriented environment.
- Experience with generative AI is a plus.
Preferred Qualifications
- Master's or PhD in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).
- Hands-on experience with building and deploying Machine Learning models for real time inference.