Data Scientist (LLM Engineer)

Description

Join GlobalLogic, to be a valid part of the team working on a huge software project for the world-class company providing M2M / IoT 4G/5G modules e.g. to the automotive, healthcare and logistics industries. Through our engagement, we contribute to our customer in developing the end-user modules firmware, implementing new features, maintaining compatibility with the newest telecommunication and industry standards, as well as performing analysis and estimations of the customer requirements.

Requirements

Bachelor's or master's degree in computer science, Data Science, Machine Learning, or a related field.

4+ years of experience working with large language models (LLMs) or NLP-related technologies.

Solid understanding of prompt engineering principles and experience in optimizing prompts for tasks such as text generation, classification, and summarization.

Strong proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, Transformers, or similar frameworks.

Experience with large-scale model training and fine-tuning for NLP tasks using tools like Hugging Face, OpenAI, or Google AI.

Solid understanding of natural language processing (NLP) techniques and algorithms, including tokenization, embeddings, transformers, and attention mechanisms.

Familiarity with cloud computing platforms (e.g., AWS, Google Cloud, Azure) for model training and deployment.

Experience in data preprocessing and handling large, unstructured text datasets.

Strong problem-solving skills and the ability to work with complex, unstructured data.

Good communication skills, with the ability to present technical concepts to both technical and non-technical stakeholders.

Experience with transformer-based architectures such as GPT-3, BERT, T5, or similar models.

Knowledge of distributed computing and working with large-scale training infrastructure (e.g., using GPUs/TPUs).

Familiarity with MLOps practices for model versioning, deployment, and monitoring.

Experience with model explainability techniques, model debugging, and interpretability.

Exposure to fine-tuning large models for specific industries or use cases (e.g., healthcare, finance).

Contributions to open-source projects or research publications in the NLP/ML domain.

Job responsibilities

About Role:

As an LLM Engineer, you will work with cutting-edge NLP and machine learning technologies to build, fine-tune, and deploy state-of-the-art language models. You will collaborate with cross-functional teams to create innovative solutions that leverage the power of large language models for a variety of applications.

Key Responsibilities:

Design, develop, and fine-tune large language models (LLMs) for various NLP tasks, such as text classification, sentiment analysis, language generation, question answering, summarization, and more.

Collaborate with data scientists, researchers, and software engineers to integrate LLMs into production environments and ensure they meet performance, scalability, and reliability requirements.

Conduct prompt engineering experiments to improve model outputs, including exploring different prompt formats, phrasing, and model configurations.

Experiment with and implement the latest advancements in natural language processing (NLP) and machine learning to improve model performance and efficiency.

Preprocess and clean large-scale text datasets, ensuring they are suitable for training and fine-tuning LLMs.

Optimize LLMs for speed, accuracy, and memory efficiency, ensuring that they can run effectively in a production environment.

Evaluate and benchmark different LLM architectures, such as transformer-based models (e.g., GPT, BERT, T5), and apply appropriate strategies for different business needs.

Conduct testing, validation, and troubleshooting of models to ensure robustness and reliability.

Stay up-to-date with developments in the field of NLP, deep learning, and large language models to ensure that our solutions remain competitive and innovative.

Collaborate with product managers and other stakeholders to define project goals, requirements, and timelines.