NLP AI Engineer

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.

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.

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.

• 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.

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

• 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.
• Knowledge of Retrieval-Augmented Generation (RAG), vector databases, knowledge graphs, and AI agent frameworks such as LangChain, LlamaIndex, or LangGraph.
• Experience with synthetic data generation, Responsible AI, AI governance, fairness, and model safety.
• Contributions to open-source LLM training frameworks, AI research, patents, or technical publications.
• Experience deploying AI applications using Kubernetes, Docker, Ray, and enterprise MLOps platforms.