NLP AI Engineer

Bright Vision Technologies•Apex, MO

2d•$130,000 - $180,000•Remote

About The 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.

Requirements

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.

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.

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.