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Mid. AI Engineer

Salary undisclosed

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Job Description

  • 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.
  • Develop and optimize machine learning models for forecasting and computer vision tasks using state-of-the-art algorithms and techniques.
  • Build and maintain scalable AI pipelines for data collection, preprocessing, feature engineering, model training, validation, and deployment.
  • Conduct applied research to implement and integrate AI methodologies, particularly in computer vision and time-series forecasting.
  • Optimize model performance for accuracy, scalability, and production readiness.
  • Process and clean diverse datasets to ensure high-quality inputs for robust machine learning models.
  • Implement machine learning models using Python and frameworks such as TensorFlow, PyTorch, or similar libraries.
  • Conduct rigorous testing and evaluation to ensure models perform well in real-world scenarios.
  • Integrate AI models with existing software systems, APIs, and databases to enhance product functionality.
  • Monitor and improve model performance post-deployment by analyzing feedback and updating models as necessary.

Job Requirements

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

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