Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is seeking an NLP AI Engineer to design, fine-tune, optimize, and deploy enterprise-scale large language models. The role leads scalable training and evaluation, builds production AI workflows, and collaborates across research, engineering, data science, and product teams.
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
Skills
- 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
- 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
- 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
Benefits
- 100% remote (U.S.)
- Tremendous career growth potential
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