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# NLP AI Engineer

- **Company:** Bright Vision Technologies
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $100,000.0 - $150,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Distributed Computing Environment, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Tensorflow, Pytorch, Large Language Models, Model Validation, Information Technology, Optimization Algorithms, Free and Open-Source Software, GPT, Data Generation
- **Published:** July 30, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=37a2a947d42583ab

## About the Role

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position., * Master's or PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).

 * 6+ years of ML engineering or AI research experience with LLMs.
 * Strong Python and PyTorch development skills.
 * Experience fine-tuning transformer-based language models.
 * Knowledge of distributed training (FSDP, ZeRO, pipeline parallelism).
 * Experience with RLHF, DPO, or similar preference optimization techniques.
 * Strong communication and problem-solving skills., * Publications in top-tier ML conferences.
 * Experience with multimodal AI models.
 * Knowledge of synthetic data generation and responsible AI.
 * Open-source contributions to LLM training frameworks.

## Description

Bright Vision Technologies is seeking an experienced NLP AI Engineer to design, fine-tune, and deploy Large Language Models (LLMs). The ideal candidate will have expertise in LLM fine-tuning, PyTorch, distributed training, RLHF/DPO, and model evaluation, with experience building scalable AI solutions for production., * Fine-tune LLMs using Supervised Fine-Tuning (SFT), DPO, RLHF, LoRA, and QLoRA.

 * Build and optimize distributed training pipelines using modern ML frameworks.
 * Develop high-quality datasets and evaluation frameworks for LLM performance.
 * Manage large-scale GPU training, model optimization, and experiment tracking.
 * Implement model safety, benchmarking, and performance improvements.
 * Collaborate with cross-functional teams and mentor junior engineers.


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