Ringside Talent Acquisition Partners
Machine Learning Engineer, LLM
Ringside Talent Acquisition Partners, Chicago, Illinois, United States, 60290
Job Description
We are looking for a
Machine Learning Engineer, LLM
to work for our client. The ideal candidate aligns with the responsibilities and qualifications outlined below.
Responsibilities: Design, develop, and deploy scalable machine learning models with a focus on large language models (LLMs). Fine-tune and optimize pre-trained LLMs (e.g., GPT, LLaMA, Mistral) for specific use cases and domains. Collaborate with data scientists, engineers, and product teams to integrate LLMs into production systems. Conduct experiments to evaluate model performance, interpretability, and fairness. Stay current with the latest research in NLP, deep learning, and generative AI, and apply findings to improve model capabilities. Build and maintain data pipelines and infrastructure for training and inference. Contribute to the development of internal tools and frameworks for model training, evaluation, and deployment. Qualifications:
3+ years of experience in machine learning or NLP, with a strong focus on deep learning. Hands-on experience with LLMs and transformer-based architectures. Proficiency in Python and ML frameworks such as PyTorch or TensorFlow. Experience with model fine-tuning, prompt engineering, and evaluation metrics for language models. Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes). Strong understanding of data structures, algorithms, and software engineering best practices.
We are looking for a
Machine Learning Engineer, LLM
to work for our client. The ideal candidate aligns with the responsibilities and qualifications outlined below.
Responsibilities: Design, develop, and deploy scalable machine learning models with a focus on large language models (LLMs). Fine-tune and optimize pre-trained LLMs (e.g., GPT, LLaMA, Mistral) for specific use cases and domains. Collaborate with data scientists, engineers, and product teams to integrate LLMs into production systems. Conduct experiments to evaluate model performance, interpretability, and fairness. Stay current with the latest research in NLP, deep learning, and generative AI, and apply findings to improve model capabilities. Build and maintain data pipelines and infrastructure for training and inference. Contribute to the development of internal tools and frameworks for model training, evaluation, and deployment. Qualifications:
3+ years of experience in machine learning or NLP, with a strong focus on deep learning. Hands-on experience with LLMs and transformer-based architectures. Proficiency in Python and ML frameworks such as PyTorch or TensorFlow. Experience with model fine-tuning, prompt engineering, and evaluation metrics for language models. Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes). Strong understanding of data structures, algorithms, and software engineering best practices.