What are the responsibilities and job description for the GenAI Engineer position at Infinite Computer Solutions (ICS)?
Job Details
Job Title: GenAI Engineer
Location: Dallas, TX
Employment Type: Full-time
Job Description :
1. Model Building: It mentions frameworks like TensorFlow and PyTorch, commonly used for developing generative models like GANs, VAEs, and Transformers.
2. Training and Fine-Tuning: It covers the training of models and fine-tuning pre-trained models, which is crucial for Generative AI applications.
3. Quantization: It includes model optimization and quantization, making Generative AI models more efficient and faster for deployment.
4. Deployment: It emphasizes practical deployment and performance optimization, which are crucial in generative applications.
5. Tech Stack: The JD specifies relevant tools and libraries like Docker, Kubernetes, and cloud platforms for productionizing Generative AI models.
Key Responsibilities:
- Develop and implement generative AI models for various applications, leveraging state-of-the-art techniques in deep learning and machine learning.
- Design and build end-to-end pipelines for data preprocessing, model training, fine-tuning, and quantization.
- Optimize model performance, accuracy, and efficiency through quantization and fine-tuning techniques.
- Collaborate with data scientists, ML engineers, and software developers to integrate generative models into existing platforms and applications.
- Conduct model evaluation, validation, and performance analysis to ensure optimal accuracy and efficiency.
- Implement model compression techniques to deploy lightweight and efficient AI solutions.
- Stay up-to-date with the latest research and advancements in generative AI and deep learning.
- Document model architecture, training processes, and performance benchmarks.
- Troubleshoot and resolve model performance issues and deployment challenges.
Technical Skills:
- Proven experience in building and training generative AI models using frameworks such as TensorFlow, PyTorch, or JAX.
- Proficiency in fine-tuning pre-trained models and applying transfer learning techniques.
- Hands-on experience with model quantization techniques to optimize inference performance.
- Expertise in deep learning techniques, including GANs, VAEs, Transformers, and Diffusion Models.
- Strong programming skills in Python, with experience in libraries such as NumPy, Pandas, and Scikit-learn.
- Familiarity with data preprocessing and augmentation methods.
- Knowledge of MLOps practices and deploying models in production environments.
- Experience with cloud platforms like AWS, Google Cloud Platform, or Azure for model training and deployment.
- Proficient in using tools like Docker, Kubernetes, and version control systems (Git).
