JOB DESCRIPTION :

- Design, develop, and deploy machine learning and deep learning models for classification, regression, recommendation, NLP, computer vision, and/or generative AI tasks.

- Build end-to-end ML pipelines: data preprocessing, model training, validation, evaluation, and deployment.

- Implement state-of-the-art algorithms using frameworks such as PyTorch, TensorFlow, or similar.

- Conduct experiments with LLMs and foundation models (e.g., GPT, BERT, Stable Diffusion, etc.).

- Collaborate with data engineers, product teams, and stakeholders to translate business requirements into ML solutions.

- Optimize model performance and scalability for production environments.

- Stay up-to-date with the latest research and developments in AI/ML/DL/Generative AI.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Mathematics, or related field.

- Experience in machine learning, deep learning, or AI product development.

- Proficiency in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers, etc.

- Experience working with generative models (GANs, VAEs, diffusion models, or LLMs).

- Strong understanding of ML model lifecycle: training, tuning, evaluation, and deployment.

- Experience with MLOps tools (e.g., MLflow, Docker, Kubeflow) is a plus.

- Familiarity with cloud platforms (AWS, Azure, or GCP) is preferred.

- Basic understanding of web development and APIs for integrating ML models into applications.

Nice to Have :

- Experience with prompt engineering or fine-tuning large language models.

- Contributions to open-source AI/ML projects or relevant publications.

- Exposure to data annotation, feature engineering, and model interpretability tools.

- Proficiency in C++ for performance optimization, model deployment, or systems-level programming in AI/ML applications.