Roles & responsibilities:
We are seeking a highly motivated AI Engineer AI with a keen interest in Generative AI and Data Science. This role is designed for fresh graduates or entry-level professionals who want to build expertise in AI model development, data analytics, and cutting-edge AI technologies. You will work closely with senior engineers and data scientists on real-world projects, gaining hands-on experience in AI research, model development, and deployment-
- Assist in developing and fine-tuning Generative AI models (e.g., GPT, GANs, VAEs, Diffusion models).
- Work with large datasets to preprocess, clean, and analyze data for AI applications.
- Develop and optimize machine learning models for various AI-driven solutions.
- Collaborate with cross-functional teams to integrate AI models into products and services.
- Conduct literature reviews and research on the latest AI advancements.
- Write clean, efficient, and well-documented code for AI models and data pipelines.
- Participate in brainstorming and problem-solving sessions to enhance AI solutions.
- Test and validate AI models for accuracy, efficiency, and robustness.
- Stay updated with emerging trends in AI, deep learning, and data science.
Required Skills & Qualifications:
- Bachelor s/Master s degree in Computer Science, Data Science, AI, Machine Learning, Mathematics, or a related field.
- Strong programming skills in Python (TensorFlow, PyTorch, NumPy, Pandas, Scikit-learn).
- Knowledge of deep learning architectures and experience with neural networks.
- Familiarity with NLP, image processing, and generative models.
- Understanding of data preprocessing, feature engineering, and statistical analysis.
- Experience with SQL, NoSQL databases, and cloud platforms (AWS, Azure, GCP) is a plus.
- Strong problem-solving and analytical skills with a passion for AI and innovation.
- Excellent communication and teamwork abilities.
Preferred Qualifications:
- Hands-on experience with Transformer models (GPT, BERT, LLaMA, etc.).
- Knowledge of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
- Familiarity with MLOps, model deployment, and API development.
- Participation in AI hackathons, research projects, or open-source contributions.