Machine Learning Engineer - NLP & Graph Analytics

Qualifications and Skills :

Educational Background : 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.

Programming Proficiency : Expert-level programming skills in Python and a strong command of its data science and machine learning ecosystem (e.g., Pandas, NumPy, Scikit-learn).

Deep Learning Expertise : Proven experience in building and deploying deep learning models using PyTorch.

Natural Language Processing (NLP) : Demonstrable experience with NLP techniques and libraries, with specific expertise in using the Stanford NLP toolkit.

Large Language Models (LLMs) : Hands-on experience with and a strong understanding of large language models, including practical experience with models like Llama 3.2.

Data Handling : Strong proficiency in handling and parsing various data formats, with specific experience in JSON extraction and manipulation.

API Development : Solid experience in developing and deploying models via RESTful APIs using frameworks like Flask, FastAPI, or Django.

Graph Database Knowledge : Experience with graph databases, particularly Neo4j, and understanding of graph data modeling and querying (Cypher).

Problem-Solving Skills : Excellent analytical and problem-solving abilities with a talent for tackling complex and ambiguous challenges.

Preferred Qualifications :

• Published research in top-tier machine learning or NLP conferences.
• Experience with MLOps tools and cloud platforms (e.g., AWS, GCP, Azure).
• Familiarity with other NLP libraries and frameworks (e.g., spaCy, Hugging Face Transformers).
• Contributions to open-source machine learning projects.

(ref : hirist.tech)