Machine Learning Engineer - NLP & Graph Analytics

<p><p><b>Qualifications and Skills :</b><br/><br/><b>Educational Background :</b> A Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

A Bachelor's degree with significant relevant experience will also be considered.<br/><br/><b>Programming Proficiency :</b> Expert-level programming skills in Python and a strong command of its data science and machine learning ecosystem (e.g., Pandas, NumPy, Scikit-learn).<br/><br/><b>Deep Learning Expertise :</b> Proven experience in building and deploying deep learning models using PyTorch.<br/><br/><b>Natural Language Processing (NLP) :</b> Demonstrable experience with NLP techniques and libraries, with specific expertise in using the Stanford NLP toolkit.<br/><br/><b>Large Language Models (LLMs) :</b> Hands-on experience with and a strong understanding of large language models, including practical experience with models like Llama 3.2.<br/><br/><b>Data Handling :</b> Strong proficiency in handling and parsing various data formats, with specific experience in JSON extraction and manipulation.<br/><br/><b>API Development :</b> Solid experience in developing and deploying models via RESTful APIs using frameworks like Flask, FastAPI, or Django.<br/><br/><b>Graph Database Knowledge :</b> Experience with graph databases, particularly Neo4j, and understanding of graph data modeling and querying (Cypher).<br/><br/><b>Problem-Solving Skills :</b> Excellent analytical and problem-solving abilities with a talent for tackling complex and ambiguous challenges.<br/><br/><b>Preferred Qualifications :</b><br/><br/>- Published research in top-tier machine learning or NLP conferences.<br/><br/>- Experience with MLOps tools and cloud platforms (e.g., AWS, GCP, Azure).<br/><br/>- Familiarity with other NLP libraries and frameworks (e.g., spaCy, Hugging Face Transformers).<br/><br/>- Contributions to open-source machine learning projects.</p><br/></p> (ref:hirist.tech)