NLP AI Engineer

Accountabilities:: Design, fine-tune, optimize, and deploy large language models using SFT, LoRA, QLoRA, RLHF, DPO, PPO, PEFT, and related techniques. 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 infrastructure. Design and implement Retrieval-Augmented Generation pipelines, embedding models, vector search systems, 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 production-ready enterprise AI applications. Lead architecture reviews and establish best practices for LLM development, deployment, and operational excellence. Mentor junior AI engineers and contribute to technical leadership across AI engineering initiatives. Evaluate emerging NLP research, foundation models, frameworks, and technologies to identify opportunities for continuous innovation. Ensure AI solutions meet enterprise requirements for scalability, security, compliance, reliability, and operational performance. 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 Python programming skills with extensive experience using PyTorch and transformer-based architectures. Proven experience fine-tuning and deploying large language models in production environments. Strong expertise in distributed training technologies such as FSDP, DeepSpeed ZeRO, pipeline parallelism, tensor parallelism, and model parallelism. Hands-on experience with RLHF, DPO, PPO, or other preference optimization techniques. Experience using AWS, Microsoft Azure, or Google Cloud Platform for AI and machine learning workloads. Strong understanding of machine learning algorithms, deep learning, NLP, model evaluation, and MLOps practices. Excellent analytical, communication, collaboration, and technical leadership skills. Experience with multimodal AI, vision-language models, speech models, or foundation models is preferred. Knowledge of RAG, vector databases, knowledge graphs, and AI agent frameworks such as LangChain, LlamaIndex, or LangGraph is preferred. Experience with synthetic data generation, Responsible AI, AI governance, fairness, and model safety is preferred. Publications or contributions to leading AI and machine learning research, open-source frameworks, patents, or technical publications are preferred. Experience deploying AI applications with Kubernetes, Docker, Ray, or enterprise MLOps platforms is preferred. U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are eligible to apply; new H-1B sponsorship is not available. Benefits: Competitive annual salary of $130,000–$180,000, based on experience. 100% remote work within the United States. Full-time, direct W2 employment. Opportunity to work on enterprise-scale AI, NLP, and LLM initiatives. Exposure to advanced technologies including distributed training, generative AI, RAG, and agentic AI. Opportunities for technical leadership, architecture ownership, and mentoring.