Generative AI

Position: Generative AI Developer (Data Scientist / ML Specialist)

Job Type: Full-time

Experience Level: Mid Level

Overview:

We are looking for an experienced Generative AI Developer with a strong background in Data Science to join our AI-driven team. In this role, you will focus on designing, building Generative AI concepts and leveraging state-of-the-art Machine Learning techniques to solve complex problems across various domains. The ideal candidate should be highly skilled in Natural Language Processing (NLP), and have hands-on experience with Generative Models such as GPT, BERT, Stable Diffusion, and similar architectures.

Key Responsibilities:

Design and develop advanced Generative AI models for tasks such as content creation, text generation, image generation, and more.

Experience with transformer models, VAEs, GANs, and other generative architectures for different AI-driven projects.

Collaborate with cross-functional teams to integrate Generative AI models into various applications, ensuring alignment with business needs.

Work with large datasets, applying techniques such as data cleaning, feature engineering, and data augmentation.

Implement and optimize deep learning models on GPU and cloud infrastructures for large-scale training and inference.

Stay up-to-date with the latest trends and research in Generative AI and Machine Learning.

Evaluate model performance through rigorous testing, model validation, and fine-tuning.

Key Qualifications:

3+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or AI Developer.

Proven expertise in building and deploying Generative AI models (e.g., GPT, BERT, DALL-E, Stable Diffusion).

Strong programming skills in Python and proficiency with ML frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

Deep understanding of Natural Language Processing (NLP) and working experience with transformers, embeddings, and language models.

Solid experience with Deep Learning techniques, including CNNs, RNNs, and attention mechanisms.

Practical experience with data preprocessing, feature engineering, and handling large-scale datasets.

Familiarity with Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other generative models.

Demonstrated ability to experiment with different model architectures and optimize for performance.