AI/ML Lead - Deep Learning Models

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. 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.