ML Ops (Machine Learning Engineer)

0-2 years
a month ago 13 Applied
Job Description

Job Title: Machine Learning Engineer

  • Model Development: Develop and implement machine learning models for various applications, such as natural language processing, computer vision, recommendation systems, and more.
  • Data Preprocessing: Perform data collection, cleaning, and feature engineering to prepare datasets for model training.
  • Model Training and Tuning: Train, evaluate, and fine-tune machine learning models to achieve optimal performance. Implement techniques like hyperparameter tuning and cross-validation.
  • Deployment: Take models from development to production, ensuring they can scale efficiently and maintain high performance. Collaborate with DevOps teams for seamless model deployment.
  • Monitoring and Maintenance: Implement monitoring and maintenance strategies for deployed models to ensure ongoing accuracy and reliability.
  • Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, DevOps and product managers, to understand business requirements and deliver machine learning solutions.
  • Documentation: Maintain clear and organized documentation of code, models, and processes.


  • Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI or a related field.
  • Proven experience in designing and developing machine learning models.
  • Proficiency in programming languages like Python and libraries such as TensorFlow, PyTorch, or scikit-learn.
  • Strong understanding of deep learning, reinforcement learning, and other machine learning techniques.
  • Experience with data preprocessing, feature engineering, and data visualization.
  • Knowledge of model deployment, containerization, and orchestration (e.g., Docker, Kubernetes).
  • Familiarity with cloud platforms (e.g., AWS, GCP, or Azure) for model deployment and management.
  • Familiarity with MLOps and cloud solutions like Azure ML studio or AWS SageMaker
  • Excellent problem-solving skills and the ability to work in a collaborative team environment.
  • Strong communication skills to explain complex technical concepts to non-technical stakeholders.
  • Additional qualifications such as certifications or publications in the field of machine learning are a plus.






data preprocessing
model deployment
cloud platforms
aws sagemaker
feature engineering
azure ml studio
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