LLM Research Engineer

Role overview

Responsibilities

• check_circle Design, train, and fine-tune large language models (e.g., GPT, LLaMA, PaLM) for various applications.
• check_circle Conduct research on cutting-edge techniques in natural language processing (NLP) and machine learning to improve model performance.
• check_circle Explore advancements in transformer architectures, multi-modal models, and emergent AI behaviors.
• check_circle Collect, clean, and preprocess large-scale text datasets from diverse sources.
• check_circle Develop and implement data augmentation techniques to improve training data quality.
• check_circle Ensure data is free from bias and aligned with ethical AI standards.
• check_circle Optimize model architecture to improve accuracy, efficiency, and scalability.
• check_circle Implement techniques to reduce latency, memory footprint, and inference time for real-time applications.
• check_circle Collaborate with MLOps teams to deploy LLMs into production environments using Docker, Kubernetes, and cloud
• check_circle Develop robust evaluation pipelines to measure model performance using key metrics like accuracy, perplexity, BLEU, and F1 score.
• check_circle Continuously test for bias, fairness, and robustness of language models across diverse datasets.
• check_circle Conduct A/B testing to evaluate model improvements in real-world applications.
• check_circle Stay updated with the latest advancements in generative AI, transformers, and NLP research.
• check_circle Contribute to research papers, patents, and open-source projects.
• check_circle Present findings and insights at conferences and internal knowledge-sharing sessions.

Basic qualifications

• Education: Master's degree with a minimum of 3+ years of experience post-graduation or a Bachelor's degree with a minimum of 5+ years of experience post-graduation
• Advanced degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Strong programming skills.
• Proficiency with Machine Learning.
• Proficiency with deep learning frameworks such as TensorFlow, PyTorch, or JAX.
• Hands-on experience with transformer-based models (e.g., GPT, BERT, RoBERTa, LLaMA).
• Expertise in natural language processing (NLP) and sequence-to-sequence models.
• Familiarity with Hugging Face libraries and OpenAI APIs.
• Experience with MLOps tools like Docker, Kubernetes, and CI/CD pipelines.
• Strong understanding of distributed computing and GPU acceleration using CUDA.
• Experience with Evaluation Pipeline Development and Large Language Model Fine-Tuning.
• Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback).

About the company