Mid. AI Engineer (Computer Vision)

Develop, deploy, and optimize AI solutions with a focus on computer vision and forecasting by building scalable machine learning models,

integrating them into production systems, and delivering data-driven solutions that address business needs.

Job Description

• Develop and optimize machine learning models for forecasting and computer vision tasks using state-of-the-art algorithms and techniques.
• Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to understand business requirement.
• 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.

• 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).
• 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.
• 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).
• Familiarity with essential libraries such as NumPy, OpenCV, Pandas, Scikit-learn, and Matplotlib.
• 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.