Language Model Architect

As an AI/ML Engineer at STGYA (part of STI Aromas), your role will involve a strong focus on training and fine-tuning Large Language Models (LLMs), Vision Language Models (VLMs), and Small Language Models (SLMs). You will have the opportunity to contribute significantly to the advancement of AI capabilities and the development of innovative AI-powered products and services. **Key Responsibilities:** - Design, implement, and optimize training pipelines for LLMs, VLMs, and SLMs using large-scale datasets. - Experiment with various training techniques such as transfer learning, reinforcement learning, and parameter-efficient fine-tuning (PEFT). - Evaluate and enhance model performance using relevant metrics and benchmarks. - Address model biases to ensure fairness and robustness. - Research and implement cutting-edge model architectures and techniques for LLMs, VLMs, and SLMs. - Develop and maintain efficient code for model training and inference. - Work with large-scale datasets including text, images, and multimodal data. - Collaborate with data engineers to ensure data quality and availability. - Optimize model training and inference for performance and scalability. - Deploy trained models to production environments and monitor their performance. - Stay updated with the latest advancements in AI/ML, specifically in LLMs, VLMs, and SLMs. - Work closely with cross-functional teams and effectively communicate technical concepts to both technical and non-technical audiences. - Document all work clearly and effectively. **Qualifications:** - Strong understanding of deep learning principles and techniques. - Extensive experience with training and fine-tuning LLMs, VLMs, and SLMs. - Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or JAX. - Experience with transformer-based architectures like BERT, GPT, T5, ViT. - Familiarity with cloud computing platforms such as AWS, GCP, or Azure. - Proficiency in Python and other relevant programming languages. - Experience with version control systems like Git. **Preferred Skills:** - Experience with distributed training and large-scale model deployment. - Knowledge of natural language processing (NLP), computer vision, and multimodal learning. - Experience with reinforcement learning, prompt engineering, model quantization, and pruning. In addition to technical skills, soft skills like strong problem-solving abilities, excellent communication, collaboration skills, a passion for AI and machine learning, and the ability to adapt to fast-changing technologies will be beneficial in this role. As an AI/ML Engineer at STGYA (part of STI Aromas), your role will involve a strong focus on training and fine-tuning Large Language Models (LLMs), Vision Language Models (VLMs), and Small Language Models (SLMs). You will have the opportunity to contribute significantly to the advancement of AI capabilities and the development of innovative AI-powered products and services. **Key Responsibilities:** - Design, implement, and optimize training pipelines for LLMs, VLMs, and SLMs using large-scale datasets. - Experiment with various training techniques such as transfer learning, reinforcement learning, and parameter-efficient fine-tuning (PEFT). - Evaluate and enhance model performance using relevant metrics and benchmarks. - Address model biases to ensure fairness and robustness. - Research and implement cutting-edge model architectures and techniques for LLMs, VLMs, and SLMs. - Develop and maintain efficient code for model training and inference. - Work with large-scale datasets including text, images, and multimodal data. - Collaborate with data engineers to ensure data quality and availability. - Optimize model training and inference for performance and scalability. - Deploy trained models to production environments and monitor their performance. - Stay updated with the latest advancements in AI/ML, specifically in LLMs, VLMs, and SLMs. - Work closely with cross-functional teams and effectively communicate technical concepts to both technical and non-technical audiences. - Document all work clearly and effectively. **Qualifications:** - Strong understanding of deep learning principles and techniques. - Extensive experience with training and fine-tuning LLMs, VLMs, and SLMs. - Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or JAX. - Experience with transformer-based architectures like BERT, GPT, T5, ViT. - Familiarity with cloud computing platforms such as AWS, GCP, or Azure. - Proficiency in Python and other relevant programming languages. - Experience with version control systems like Git. **Preferred Skills:** - Experience with distributed training and large-scale model deployment. - Knowledge of natural language processing (NLP), computer vision, and multimodal learning. - Experience with reinforcement learning, prompt engineering, model quantization, and pruning. In addition to technical skills, soft skills like strong problem-solving a