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:
Bachelor's or Master's 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.
