A Gen AI (Generative Artificial Intelligence) Engineer is responsible for designing, developing, and implementing AI models, particularly focusing on generative AI applications. This role involves working with state-of-the-art machine learning techniques, such as natural language processing (NLP), computer vision, and deep learning models, to create solutions that can generate new content, including text, images, music, and more. The Gen AI Engineer works closely with data scientists, software developers, and product teams to build scalable AI systems that meet business goals and enhance user experiences.
| Experience | 5 years |
| Salary | Market Competitive |
| Location | UAE, Dubai |
| Qualification | A Master’s degree |
| Posted | 26 September 2024 |
| Job Type | Full-Time |
| Posted by | Habeebi Recruiter |
| last date to apply | apply within 15 days |
Responsibilities
1. Design and Development of AI Models
The core responsibility of a Gen AI Engineer is to design and build generative AI models. These models may involve tasks such as generating natural language responses, creating synthetic images, or producing audio content. The engineer is responsible for selecting appropriate machine learning algorithms, building and fine-tuning deep learning architectures, and leveraging existing frameworks like GPT, GANs, or transformer models. The objective is to build models that can produce accurate, high-quality, and innovative outputs based on input data.
2. Data Collection and Preprocessing
For AI models to function effectively, they require large datasets. The Gen AI Engineer is responsible for sourcing, collecting, and preprocessing relevant data, ensuring it is clean, organized, and suitable for training models. This can involve working with structured and unstructured data types like text, images, or videos. The engineer ensures that datasets are free of bias and anomalies, optimizing them for training purposes to improve model performance and reliability.
3. Model Training and Fine-Tuning
Once models are designed, the engineer is tasked with training them using appropriate datasets. This involves feeding data through the models, adjusting weights, and optimizing hyperparameters to achieve the best possible performance. The Gen AI Engineer continuously fine-tunes models, adjusting configurations and leveraging techniques like transfer learning to improve accuracy and efficiency. Regular testing and validation of models are crucial to ensure they meet performance metrics and can operate effectively in production environments.
4. Collaborating with Cross-Functional Teams
The role of a Gen AI Engineer often involves close collaboration with various teams across the organization, such as data scientists, software developers, and product managers. By working with cross-functional teams, the engineer helps translate business needs into technical solutions, ensuring that AI models align with product requirements and deliver real value to end-users. Collaboration is key for integrating generative AI models into broader platforms or applications, such as chatbots, design tools, or creative software.
5. Integration and Deployment of AI Solutions
The Gen AI Engineer is responsible for deploying AI models into production environments where they can be used by applications or end-users. This requires ensuring that the models are scalable, efficient, and optimized for real-world use. The engineer works on integrating models with backend systems, APIs, or cloud platforms and ensures that they are accessible and maintainable. Post-deployment, the engineer monitors performance, addressing any issues or updates needed to improve or scale the solution.
6. Model Evaluation and Performance Monitoring
Evaluating the performance of AI models is an ongoing task. The Gen AI Engineer develops metrics to assess the accuracy, efficiency, and robustness of generative AI models. Regular monitoring ensures that the models are performing as expected, especially when dealing with large-scale data or real-time operations. The engineer also keeps track of any potential model drift or degradation, retraining and updating models when necessary to maintain optimal performance.
7. Research and Development
Generative AI is a rapidly evolving field, and staying updated with the latest developments is crucial. The Gen AI Engineer engages in continuous research and experimentation to explore new techniques, tools, and methodologies in AI. By keeping abreast of the latest advancements in machine learning, NLP, and computer vision, the engineer can incorporate cutting-edge solutions into their work, ensuring the company remains competitive in the field of AI innovation.
8. Ethical AI Practices
A critical responsibility for a Gen AI Engineer is ensuring that AI models are built and deployed ethically. This involves considering factors like data privacy, model fairness, and the potential social impacts of generative AI technologies. The engineer must ensure that the models are free from biases, that they comply with data protection regulations, and that they operate transparently, especially when used in applications that interact with or affect users.
Qualifications
A Gen AI Engineer typically holds a degree in computer science, artificial intelligence, data science, or a related field. Strong experience in machine learning, deep learning, and generative models like GPT, GANs, or VAEs is essential. The candidate should have a solid understanding of programming languages like Python, TensorFlow, or PyTorch, and experience with cloud platforms (AWS, Google Cloud, Azure) for deploying AI models. Excellent problem-solving skills, a strong foundation in mathematics, and an understanding of data ethics are also important for this role. Continuous learning and a passion for AI research are highly valued in this fast-evolving field.
How to apply:
Send your updated resume to our email or directly reach us at our phone:
Email: sales.apartech@apar.com
Telephone: +971 (04) 458 0599