Mid. AI Engineer

Job Description

  1. Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to understand business requirements and design impactful AI solutions focused on forecasting and computer vision.
  2. Develop and optimize machine learning models for forecasting and computer vision tasks using state-of-the-art algorithms and techniques.
  3. Build and maintain scalable AI pipelines for data collection, preprocessing, feature engineering, model training, validation, and deployment.
  4. Conduct applied research to implement and integrate AI methodologies, particularly in computer vision and time-series forecasting.
  5. Optimize model performance for accuracy, scalability, and production readiness.
  6. Process and clean diverse datasets to ensure high-quality inputs for robust machine learning models.
  7. Implement machine learning models using Python and frameworks such as TensorFlow, PyTorch, or similar libraries.
  8. Conduct rigorous testing and evaluation to ensure models perform well in real-world scenarios.
  9. Integrate AI models with existing software systems, APIs, and databases to enhance product functionality.
  10. Monitor and improve model performance post-deployment by analyzing feedback and updating models as necessary.


Job Requirements

  1. Graduate from Bachelor, Diploma 3, Diploma 4 degree from IT, Computer Science or other related majors.
  2. Have a minimum 2 years of experience as an AI Engineer with a strong focus on computer vision and/or forecasting (time-series modeling).
  3. Proficiency in programming languages such as Python, TensorFlow, PyTorch, or similar for model development and deployment.
  4. Solid understanding of machine learning techniques and algorithms, including Computer Vision (e.g., CNNs, YOLO, object detection) and Time-Series Forecasting (e.g., ARIMA, LSTM).
  5. Hands-on experience in data preprocessing, feature engineering, and creating reusable data pipelines.
  6. Experience in deploying, monitoring, and maintaining machine learning models in production.
  7. Familiarity with MLOps practices is a plus.
  8. Strong problem-solving and critical thinking skills, with the ability to work effectively in collaborative, cross-functional teams.
  9. Excellent communication skills to convey technical concepts to both technical and non-technical stakeholders.

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