Machine Learning Engineer
4 days ago
Role
A Machine Learning Engineer for developing, deploying, and maintaining scalable machine learning solutions that drive business intelligence, automation, and predictive analytics. This role bridges data science and software engineering, bringing AI models into impactful, real-world applications
Key Responsibilities
- Design, build, and deploy machine-learning models to solve business problems.
- Collaborate with data scientists, software engineers, and product teams to integrate ML models into applications.
- Develop and optimize algorithms for accuracy, speed, and scalability.
- Manage data preprocessing, feature engineering, and model evaluation.
- Monitor production ML systems; update models based on feedback and performance metrics.
- Participate in code reviews, technical discussions, and mentor junior team members.
- Utilize ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) for development.
- Ensure data pipelines are robust and scalable for machine learning workflows.
Required Skills
- Bachelors or Master's degree in Computer Science, Engineering, Mathematics, or related field.
- 4–5 years of hands-on experience building and deploying machine-learning models.
- Proficient in Python and at least one additional programming language (e.g., Java, C++).
- Solid understanding of supervised and unsupervised ML algorithms, NLP or computer vision techniques.
- Experience with version control tools (e.g., Git) and containerization (Docker, Kubernetes) is desirable.
- Analytical mindset and problem-solving ability.
- Excellent communication and collaboration skills.
Preferred Skills
- Experience with cloud deployment strategies (AWS, GCP, Azure).
- Familiarity with MLOps practices and data engineering principles.
- Exposure to big data systems (e.g., Spark) and data visualization techniques.
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