NLP AI Engineer

🏢 Bright Vision Technologies · all Bright Vision Technologies jobs
📍 United States
💰 USD 130,000 - 180,000 / annual
📅 Posted 2026-08-30 · via Himalayas
🏷 NLP-Engineer,LLM-Engineer,Machine-Learning-Engineer,Deep-Learning-Engineer,AI-Research-Engineer,NLP-AI-Engineering,AI-NLP-Engineering,Senior-NLP-Engineer,NLP-Engineering,AI-Language-Engineer,AI-LLM-Engineer
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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 agentic AI workflows.

- Develop automated benchmarking, safety testing, hallucination detection, and Responsible AI evaluation frameworks.

- Collaborate with AI researchers, software engineers, data scientists, and product teams to deliver enterprise AI applications.

- Lead architecture reviews, establish best practices for LLM development, and mentor junior AI engineers.

- Evaluate emerging NLP research, foundation models, and AI frameworks to drive continuous innovation.

- Ensure AI solutions meet enterprise requirements for scalability, security, compliance, and operational excellence.

Required Qualifications

- Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related technical discipline (or equivalent professional experience).

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10+ years of professional experience in Artificial Intelligence, Machine Learning, NLP, or LLM engineering.

- Expert-level programming skills in Python with extensive experience using PyTorch and transformer-based architectures.

- Proven experience fine-tuning and deploying Large Language Models (LLMs) for production environments.

- Strong expertise in distributed training technologies, including FSDP, DeepSpeed ZeRO, pipeline parallelism, tensor parallelism, and model parallelism .

- Hands-on experience with RLHF , DPO , PPO , or other preference optimization techniques.

- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP) for AI workloads.

- Strong understanding of machine learning algorithms, deep learning, NLP, model evaluation, and MLOps practices.

- Excellent analytical, communication, collaboration, and technical leadership skills.

Preferred Qualifications

- Publications in leading AI and Machine Learning conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or CVPR .

- Experience with multimodal AI , vision-language models (VLMs), speech models, or foundation models.

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