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 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). • 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. • 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to jaya@bvteck.com or contact us at (908) 505-3545. Learn more about Bright Vision Technologies at www.bvteck.com. Bright Vision Technologies is an Equal Opportunity Employer. Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Requirements

Must-haves

NLP AI Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
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.
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.

Nice-to-haves

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.

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Keywords to mirror

aimachine learningllmpythonawsazuregcpkubernetesdocker

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