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NLP AI Engineer

Bright Vision Technologies•United States•🌍 Remote
Full-timeSenior
$130K - $180K
per year
👁️ 0 views•📝 0 applications•Posted 10/22/2026•Expires 12/21/2026
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Job Description

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title NLP AI Engineer Location: 100% Remote (U. S.) Position Type: Full-time, Direct W2 Salary Range: $130,000–$180,000 Annually (based on experience) Experience Required: 10+ Years 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.

Job Summary

Bright Vision Technologies is seeking a highly experienced NLP AI Engineer with 10+ years of experience in Artificial Intelligence, Machine Learning, and Natural Language Processing (NLP) to design, fine-tune, optimize, and deploy enterprise-scale Large Language Models (LLMs) .

The ideal candidate will possess deep expertise in PyTorch, transformer architectures, distributed training, RLHF, Direct Preference Optimization (DPO), model evaluation, and MLOps , with a proven track record of building scalable, production-ready AI solutions.

This role requires strong technical leadership and collaboration across AI research, engineering, and product teams.

Key Responsibilities

Design, fine-tune, and optimize Large Language Models using techniques such as Supervised Fine-Tuning (SFT), LoRA, QLoRA, RLHF, DPO, PPO, and parameter-efficient fine-tuning (PEFT) . Architect scalable distributed training pipelines using modern deep learning frameworks and GPU clusters.

Develop high-quality datasets, synthetic data generation pipelines, and evaluation frameworks to improve model accuracy, robustness, and reliability. Optimize large-scale GPU training, inference performance, experiment tracking, and model serving.

Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding models, vector search, and

Required Skills

Deep LearningLeadershipMachine LearningNLPPyTorchSoftware Engineering

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