Design and implement NLP algorithms to solve complex language processing tasks, such as language correction, text classification, sentiment analysis, entity recognition, and information extraction.
Develop and maintain Large Language Models (LLM) by training, fine-tuning, and evaluating models on large-scale datasets.
Collaborate with cross-functional teams to understand business requirements and translate them into scalable and efficient NLP solutions.
Optimize and streamline ML workflows by implementing Dev-Ops practices, including version control, continuous integration and deployment (CI/CD), and automated testing.
Apply best practices in ML engineering to ensure model robustness, scalability, and maintainability.
Conduct exploratory data analysis, feature engineering, and model selection to improve the performance of NLP and ML models.
Stay up-to-date with the latest advancements in NLP, LLM, and ML Dev-Ops, and actively contribute to the research community through publications, conferences, and open-source projects.
Qualifications:
Degree in Computer Science, Artificial Intelligence, or a related field.
Experience working with various operating systems, including Linux.
Strong proficiency in developing and maintaining Large Language Models (LLM), including hands-on experience with fine-tuning and deployment.
Extensive experience in Natural Language Processing (NLP) techniques and frameworks, such as spaCy, NLTK, Transformers (e.g., BERT, GPT), and word embeddings.
Solid understanding of Machine Learning (ML) concepts and algorithms, such as supervised and unsupervised learning, deep learning, and reinforcement learning.
Proven experience in ML Dev-Ops, including version control (e.g., Git), containerization (e.g., Docker), cloud deployment (e.g., AWS), and CI/CD pipelines.
Proficiency in programming languages commonly used in data science and ML, such as Python & C++.
Familiarity with data structures and classes in order to efficiently organize, manipulate, and represent data during the development of NLP and ML solutions.
Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
Knowledge of Regular Expressions for text processing tasks, such as pattern matching, extraction, and text manipulation.
Excellent communication skills with the ability to effectively present complex technical concepts to both technical and non-technical stakeholders.
Experience with distributed computing frameworks (e.g., Apache Spark) and big data processing tools (e.g., Hadoop, Hive) is a plus.
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