Back
Niche ML/LLM and Neo4j requirements reduce candidate competition.
Requires specialized ML/LLM, PyTorch, NLP, and Neo4j experience; low cross-industry transferability.
Advanced degree preference plus many mandatory ML, LLM, and system skills creates high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and implement end-to-end machine learning models including data preprocessing, feature engineering, training, and evaluation using Python.
Build and fine-tune deep learning neural networks for NLP tasks using PyTorch and Stanford NLP library; work with Large Language Models (LLMs) like Llama 3.2 for text-based AI applications.
Develop and maintain scalable RESTful APIs to deploy ML models in production; perform complex data engineering including JSON extraction and graph database modeling with Neo4j.
Master's or Ph.D. in Computer Science, AI, Machine Learning, or related field; Bachelor's degree with significant relevant experience also considered.
Expert-level programming in Python with strong knowledge of its ML ecosystem (Pandas, NumPy, Scikit-learn).
Proven experience with deep learning frameworks, specifically PyTorch, and NLP using Stanford NLP toolkit.
Experience with Large Language Models (Llama 3.2), RESTful API development, JSON data processing, and graph databases (Neo4j). Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end ML model lifecycle management, particularly in NLP and deep learning domains.
Skilled in integrating complex AI models into production environments through API development and MLOps practices.
Proficient in advanced data analytics and graph data modeling, indicating strong data engineering and problem-solving capabilities.