NLP Engineer

Bright Vision Technologies•Bridgewater Township, NJ

1d•Remote

About The Position

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge cloud data platform technologies to design scalable, secure, and high-performance analytics environments. As we continue to grow, we’re looking for a skilled NLP Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Requirements

Python
NLTK
spaCy
Transformers
Hugging Face libraries
BERT/GPT-based models
RESTful APIs
Cloud Platforms (AWS, Azure, GCP)
Docker
Kubernetes
Linux
CI/CD pipelines
Git
Agile

Responsibilities

Develop and deploy advanced Natural Language Processing (NLP) solutions using Python, NLTK, and spaCy for text preprocessing, tokenization, and feature extraction.
Leverage Transformers and Hugging Face libraries to build, train, and fine-tune BERT/GPT-based models for tasks like Text Classification, Named Entity Recognition (NER), Sentiment Analysis, and Information Extraction.
Design and execute Model Training & Fine-Tuning pipelines, optimizing hyperparameters and evaluating performance on large-scale datasets.
Create scalable RESTful APIs to serve NLP models, enabling seamless integration with web applications and microservices.
Deploy models on Cloud Platforms (AWS, Azure, GCP) using containerization with Docker and orchestration via Kubernetes for high availability.
Manage Linux-based environments, automating deployments through CI/CD pipelines and Git for version control and collaboration.
Collaborate in Agile methodologies, participating in sprints to deliver production-ready NLP features iteratively.
Implement end-to-end NLP workflows, from data ingestion to inference, ensuring robustness, scalability, and low-latency predictions.
Optimize model inference for real-time applications, incorporating techniques like quantization and distillation.
Conduct research and experimentation with state-of-the-art NLP models to drive innovation in language understanding and generation.