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Artificial Intelligence Development Specialist
3 weeks ago
As an Artificial Intelligence Development Specialist, you will play a vital role in shaping the future of our organization by developing and implementing cutting-edge AI solutions. This is an exceptional opportunity to leverage your technical expertise and creativity to drive innovation and growth.
- At least 3 years of experience in machine learning, deep learning, or AI research, with a focus on generative models.
- Proficiency in generative models such as GANS (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based models (e.g., GPT-3/4, BERT, DALL.E).
- Strong understanding of model fine-tuning, transfer learning, and prompt engineering in the context of large language models (LLMs).
- Excellent programming skills in Python and familiarity with relevant libraries and frameworks.
- Hands-on experience in building data applications using Streamlit or similar tools.
- Deep understanding of transformer architecture, including multi-head attention, layer normalization, and residual connections.
- Proficiency with Hugging Face Transformers, PyTorch, or TensorFlow.
- Experience in training and deploying LLMs for tasks like text generation, summarization, or translation.
- Familiarity with AdamW, learning rate warm-up, and optimization strategies.
- Exposure to advanced transformer variants (e.g., Transformer-XL, Longformer, BigBird, LoRA).
- Solid debugging and performance tuning skills.
Our organization offers a dynamic and collaborative work environment that fosters creativity, innovation, and growth. As an Artificial Intelligence Development Specialist, you will have access to:
- State-of-the-art infrastructure and resources to support AI development and deployment.
- Ongoing training and professional development opportunities to stay up-to-date with the latest AI trends and technologies.
- Collaborative teams and mentors who are passionate about AI and committed to supporting your growth and success.
If you have experience with Docker, Kubernetes, and MLOps, knowledge of CI/CD pipelines and model deployment best practices, this would be a great advantage but not required.
