NLP AI Engineer

Location: 100% remote within the United States

Salary: $130,000–$180,000 annually, based on experience

Company

Technology consulting and software development organization delivering cloud, AI, data, and enterprise solutions across the United States.

What you will do

• Design, fine-tune, optimize, and deploy production-scale large language models using SFT, LoRA, QLoRA, RLHF, DPO, PPO, and PEFT.
• Architect distributed training pipelines across GPU clusters and optimize training, inference, experiment tracking, and model serving.
• Build datasets, synthetic data generation pipelines, benchmarking systems, and model evaluation frameworks.
• Implement RAG pipelines, embedding models, vector search, and agentic AI workflows.
• Develop safety testing, hallucination detection, Responsible AI, and reliability evaluation capabilities.
• Lead architecture reviews, establish LLM engineering practices, collaborate across research and product teams, and mentor junior engineers.

Requirements

• 10+ years of professional experience in AI, machine learning, NLP, or LLM engineering.
• Master’s or Ph.D. in computer science, AI, machine learning, NLP, or a related technical discipline, or equivalent professional experience.
• Expert Python programming skills with extensive PyTorch and transformer architecture experience.
• Production experience fine-tuning and deploying LLMs, including distributed training with FSDP, DeepSpeed ZeRO, pipeline, tensor, or model parallelism.
• Hands-on experience with RLHF, DPO, PPO, or other preference optimization techniques, plus cloud AI workloads on AWS, Azure, or GCP.
• U.S. work authorization is required. New H-1B visa petitions cannot be sponsored. U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates may apply.

Nice to have

• Publications in leading AI and machine learning conferences or contributions to open-source LLM frameworks, research, patents, or technical publications.
• Experience with multimodal AI, vision-language models, speech models, or foundation models.
• Knowledge of vector databases, knowledge graphs, LangChain, LlamaIndex, or LangGraph.
• Experience with synthetic data, Responsible AI, governance, fairness, model safety, Kubernetes, Docker, Ray, or enterprise MLOps platforms.

Culture & Benefits

• Fully remote work within the United States.
• Direct full-time W-2 employment.
• Opportunity to work on enterprise-scale cloud, AI, data, and software solutions.
• Collaboration with AI researchers, software engineers, data scientists, and product teams.

Hiring process

• Submit a resume for consideration by email or through the company’s application process.