Início Arábia Saudita Artificial Intelligence Engineer

Início Arábia Saudita Artificial Intelligence Engineer

Artificial Intelligence Engineer

Full time na Almajdouie no Saudi Arabia
Publicado em January 25, 2025

Detalhes do emprego

1- Developing AI Models:

  1. Model Creation: Designing and building machine learning models to solve specific problems.
  2. Model Training: Training models using large datasets to ensure accuracy and efficiency.
  3. Model Deployment: Transforming models into APIs for integration with other systems.
2- Data Management:
  1. Data Collection: Gathering and preprocessing data from various sources.
  2. Data Cleaning: Ensuring data quality by removing inconsistencies and errors.
  3. Data Storage: Managing databases and data warehouses to store large volumes of data.
3- Statistical Analysis:
  1. Data Analysis: Using statistical methods to analyze data and extract meaningful insights.
  2. Performance Metrics: Evaluating model performance using metrics like accuracy, precision, recall, and F1 score.
  3. Optimization: Fine-tuning models to improve performance and efficiency.
4- Collaboration:
  1. Cross-functional Teams: Working with data scientists, software engineers, and product managers.
  2. Stakeholder Communication: Explaining complex AI concepts to non-technical stakeholders.
  3. Project Management: Leading AI projects from conception to deployment.
Skills: 1- Programming:
  1. Languages: Proficiency in Python, R, Java, and C++.
  2. Frameworks: Experience with TensorFlow, PyTorch, and Scikit-learn.
2- Mathematics:
  1. Statistics: Understanding probability, hypothesis testing, and statistical modeling.
  2. Linear Algebra: Knowledge of vectors, matrices, and tensor operations.
  3. Calculus: Familiarity with differentiation and integration for optimization algorithms.
3- Big Data Technologies:
  1. Tools: Knowledge of Apache Spark, Hadoop, and MongoDB.
  2. Data Processing: Experience with ETL (Extract, Transform, Load) processes.
4- Machine Learning Algorithms:
  1. Supervised Learning: Techniques like linear regression, decision trees, and support vector machines.
  2. Unsupervised Learning: Algorithms such as k-means clustering and principal component analysis.
  3. Deep Learning: Understanding neural networks, convolutional neural networks (CNNs), and recurrent neural networks.
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