IT and Engineering > Artificial Intelligence Engineer
Salary National Average
| Low | Medium | High |
|---|---|---|
| 107680.0000 | 133900.0000 | 171410.0000 |
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Candidate Supply: 14,093 Job Openings: 5,229
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Short Description:
An AI Engineer, also known as an Artificial Intelligence Engineer, is a specialized professional in the field of artificial intelligence and machine learning. They are responsible for developing and implementing AI solutions and algorithms that enable machines and systems to perform tasks that typically require human intelligence. AI Engineers work in various industries, including healthcare, finance, robotics, and more, to create intelligent software and systems that can analyze data, make predictions, and automate processes.
Duties / Responsibilities:
- Design, develop, and deploy machine learning models and AI algorithms for specific applications.
- Collect and preprocess data, ensuring its quality and suitability for AI model training.
- Collaborate with data scientists and domain experts to understand business or problem-specific requirements.
- Implement and optimize deep learning models, natural language processing (NLP) models, and computer vision algorithms.
- Tune and fine-tune AI models to achieve the desired level of accuracy and performance.
- Evaluate and compare the effectiveness of different AI and machine learning techniques.
- Stay up-to-date with the latest developments in AI research and technology to incorporate new methodologies.
- Troubleshoot and debug issues related to AI models and their integration into applications.
- Collaborate with software developers to integrate AI capabilities into software and systems.
- Maintain documentation of AI model development, implementation, and performance for reporting and transparency.
Skills / Requirements / Qualifications
- Education: Bachelor's or master's degree in computer science, artificial intelligence, machine learning, or a related field; a Ph.D. may be required for research-oriented roles.
- Programming: Strong programming skills, with expertise in languages such as Python, R, or TensorFlow.
- AI Frameworks: Proficiency in AI and machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Statistics: Solid understanding of statistical analysis, data structures, and algorithms.
- Problem-Solving: Excellent problem-solving skills and a data-driven mindset.
- AI Techniques: Knowledge of deep learning, neural networks, and various AI techniques.
- Communication: Strong communication skills for collaborating with cross-functional teams and explaining AI concepts to non-technical stakeholders.
- Modeling: Experience with data manipulation, feature engineering, and model deployment in real-world applications.
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