OpenAIRemote · Paris, France
5h ago
JobgetherPosted today
L4
NLP AI Engineer
NLP AI Engineer at Jobgether scores 95 out of 100 on AI centrality, which makes it a Level 4 role on this board.
Remote (US)seniorFull-time
AI in this role
Design, optimize, and deploy enterprise-scale NLP and LLM solutions including RAG pipelines and agentic workflows.
pythonpytorchlangchainvector-database
ragai-agentsfine-tuningnlpllmdistributed-trainingmachine-learning
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a NLP AI Engineer based in United States.
This role focuses on designing, optimizing, and deploying enterprise-scale Natural Language Processing and Large Language Model solutions.
You will work across model architecture, fine-tuning, distributed training, evaluation, inference, and production deployment.
The position combines advanced AI engineering with practical application development, including RAG pipelines, embeddings, vector search, and agentic workflows.
You will help build reliable, scalable, and secure AI systems capable of meeting demanding enterprise requirements.
The role involves close collaboration with AI researchers, software engineers, data scientists, and product teams.
You will also contribute to technical direction, architecture reviews, engineering standards, and the development of other AI engineers.
This is a highly technical opportunity for an experienced AI professional working at the forefront of modern NLP and LLM technologies.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
RagAI AgentsFine TuningNlpLlmDistributed TrainingMachine LearningPython
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Tell me about a project where llm was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Rag, AI Agents, Fine Tuning, Nlp, and Llm. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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