Over 7 years of experience in software development, with a strong focus on Python.
Possess a minimum of 3 years of pertinent experience as a Machine Learning Engineer.
In-depth knowledge of text representation techniques, including n-grams, embedding models, sentiment analysis, and proficiency in statistical and evaluation methodologies.
Familiarity with various deep learning model architectures, such as Transformers, LSTMs, and Attention-based models.
Demonstrated expertise in fine-tuning various Large Language Models (LLM), conversational AI, and generative AI.
Proficiency in machine learning frameworks, including TensorFlow, PyTorch, and Keras, as well as libraries like scikit-learn, scipy, numpy, spacy, langchain, and Llama Index.
Hands-on experience with Docker containers, MySQL, Elasticsearch, and competence in utilizing various cloud platforms.
Elementary knowledge in handling Big data would be preferable.
Key Responsibilities
Develop NLP applications, including text classification, NER, LLM fine-tuning, RAG, and more.
Identify suitable annotated datasets for use with Supervised learning methods.
Employ a range of embedding techniques tailored to specific NLP tasks.
Discover and implement appropriate algorithms and tools for various NLP tasks.
Translate data science prototypes into practical applications using suitable ML/DL algorithms and tools.
Train and fine-tune ML/DL models while conducting evaluation experiments.
Conduct statistical analyses of results and make model refinements as needed.
Stay current in the rapidly evolving field of machine learning.
Create a variety of MLOPs pipelines to optimize the performance of the NLP ecosystem.