Junior Machine Learning Engineer

Job description

We are seeking an enthusiastic and motivated Junior Machine Learning Engineer who is passionate about applying and scaling machine learning models in real-world applications. This role is perfect for individuals who are eager to dive into the dynamic field of data science and make significant contributions to impactful projects.

As a Junior Machine Learning Engineer, you will have the opportunity to work on diverse and challenging problems, leveraging your analytical skills and creativity to develop and deploy models that solve business-critical issues. You will be part of a collaborative team, where you will learn from experienced data scientists and engineers, gaining hands-on experience with the latest tools and technologies in the industry. This position offers a unique blend of technical and business exposure, allowing you to see the tangible results of your work in improving business processes and decision-making.

We are looking for candidates who are curious, detail-oriented, and have a strong foundation in statistical analysis, machine learning algorithms, and data manipulation. Your ability to communicate complex technical concepts to non-technical stakeholders will be crucial in ensuring the successful implementation and adoption of your models. If you are driven by the excitement of turning data into actionable insights and are ready to take on new challenges, we would love to hear from you.


  • Bachelor’s degree in Computer Science, Computer Engineering, or Electrical Engineering.
  • Completed coursework in fundamental Machine Learning and Deep Learning concepts.
  • Hands-on experience with a deep learning project during academic studies- an advantage
  • Eagerness to learn, contribute, and be a part of an incredible story and technology journey.


Model Training and Evaluation:

  • Develop, train, and evaluate machine learning models to meet specific business objectives.

Understanding Business Needs:

  • Collaborate with business stakeholders to understand their needs and translate them into technical requirements.
  • Ensure AI solutions align with the overall business setup.

Data Quality Assessment:

  • Evaluate the quality and suitability of data sources for training models.
  • Identify and address data quality issues to ensure accurate model performance.

Production Support:

  • Monitor and troubleshoot AI models in production, resolving issues promptly to ensure smooth operation.

Continuous Improvement:

  • Retrain and update models to maintain or enhance their performance over time.

Tool Utilization:

  • Utilize internal tools for model training, data collection, and model management.
  • Contribute to the development and enhancement of these tools to improve efficiency and effectiveness.

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