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Nlp Llm Jobs (NOW HIRING)

Consip S.P.A.

NY · On-site

$80 - $100/hr

Ottimizzazione e validazione modelli NLP/LLM * Deployment e integrazione con pipeline MLOps/LLMOps * Monitoraggio e manutenzione dei modelli in produzione * Testing di robustezza, fairness e ...

Yakamconsulting

$150 - $200/hr

As a Senior NLP and LLM Data Scientist, you will be a key player in designing, implementing, and optimizing solutions that analyze and understand human language at scale. Your work will involve ...

Select Minds LLC

Sr Data Scientist GenAI

Dallas, TX · On-site

$150K - $210K/yr

Opportunity for advancement Sr Data Scientist (NLP / LLM / Generative AI) Location: Dallas, TX Roles & Responsibilities : - Design, build, fine-tune, and deploy LLMs, transformer-based NLP models ...

Abbott Laboratories

Sr. Engineer, AI Platform

Madison, WI · On-site

$125 - $150/hr

Lead the execution of ML, NLP, LLM deliverables in support of the AI strategies. * Collaborate closely with data scientists, machine learning engineers, and software engineers to ensure smooth ...

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Nlp Llm information

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How much do nlp llm jobs pay per year?

As of Sep 9, 2026, the average yearly pay for nlp llm in the United States is $127,031.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,000.00 and $143,500.00 per year, depending on experience, location, and employer.

What is an NLP LLM?

NLP LLMs refer to Natural Language Processing (NLP) Large Language Models. These are advanced artificial intelligence systems designed to understand, generate, and interact using human language. NLP LLMs, such as GPT-4 or BERT, are trained on massive datasets and can perform tasks like translation, summarization, question answering, and text generation. They are commonly used in chatbots, virtual assistants, search engines, and many other applications that require comprehension and generation of natural language. Their capabilities are continually evolving as the technology advances.

What are the key skills and qualifications needed to thrive as an NLP LLM engineer?

To thrive as an NLP LLM Engineer, you need a strong background in machine learning, deep learning, natural language processing, and programming (often Python), typically supported by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and cloud platforms, as well as experience with large-scale data processing, are essential technical qualifications. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate and innovate in multidisciplinary teams. These competencies are crucial for developing, optimizing, and deploying advanced language models that drive real-world AI applications.

What are common challenges faced when working with NLP large language models in a production environment?

When working with NLP LLMs in a production setting, professionals often encounter challenges related to model scalability, latency, and ensuring data privacy. Handling large volumes of data efficiently and optimizing inference speed without compromising accuracy are key concerns. Additionally, integrating LLMs with existing systems and maintaining model performance as new data or requirements emerge can require ongoing collaboration with engineering and data teams. Staying updated with the latest advancements and best practices is also important for continuous improvement and security.

What is the difference between Nlp Llm vs Data Scientist?

AspectNlp LlmData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of NLP and ML frameworksDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentResearch labs, AI companies, tech firms focusing on NLP applicationsBusiness analytics, research, and development teams across various industries
Industry UsagePrimarily in AI, NLP, and machine learning sectorsAcross finance, healthcare, marketing, and tech industries

While Nlp Llm specialists focus on developing and fine-tuning large language models for natural language processing tasks, Data Scientists analyze data to extract insights and build predictive models. Both roles require strong programming skills and a background in data or AI, but Nlp Llm roles are more specialized in NLP and language models, whereas Data Scientists have a broader focus on data analysis and interpretation across industries.

What other helpful pages are available for Nlp Llm?

Other pages related to Nlp Llm:

Infographic showing various Nlp Llm job openings in the United States as of September 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 75% Physical, 5% Hybrid, and 20% Remote job distribution, with an average salary of $127,031 per year, or $61.1 per hour.

AI NLP Engineer

NY • On-site

Consip S.P.A.
See more jobs at Consip S.P.A.external

$80 - $100/hr

Other

Posted 22 days ago

Job description

Cerchiamo Persone che portino competenza nelle sfide di ogni giorno, mettendo passione in ciò che fanno e scegliendo la trasparenza come stile di lavoro e di relazione, nei processi e nel dialogo con gli stakeholder. Persone che guardino al futuro con curiosità, concretezza e spirito di innovazione, per contribuire alla crescita dell’Azienda e del Sistema Paese.

L’AI Natural Language Processing Engineer è responsabile della progettazione, dello sviluppo e dell’implementazione di sistemi in grado di comprendere, interpretare e generare linguaggio umano, garantendo la gestione di bias linguistici e culturali.

Responsabilità
  • Preparazione e gestione dati testuali
  • Sviluppo e addestramento modelli NLP e LLM
  • Ottimizzazione e validazione modelli NLP/LLM
  • Deployment e integrazione con pipeline MLOps/LLMOps
  • Monitoraggio e manutenzione dei modelli in produzione
  • Testing di robustezza, fairness e generazione responsabile
  • Documentazione tecnica e governance
  • Conformità normativa e sicurezza
  • Valutazione e mitigazione bias linguistici
  • Explainability e trasparenza dei modelli NLP/LLM
Qualifiche richieste
  • Laurea Magistrale in Informatica, Ingegneria, Matematica, Fisica o altre discipline STEM
  • Esperienza lavorativa pregressa di almeno 1 anno maturata, anche in ambito accademico, in almeno uno dei seguenti ambiti:
    • A) tecniche di embedding e rappresentazione linguistica, finalizzate alla modellazione semantica di testi per compiti di analisi o generazione del linguaggio
    • B) framework e librerie NLP, utilizzati per l’addestramento e l’inferenza di modelli NLP e/o LLM in contesti applicativi reali
    • C) tecniche di pre-processing linguistico, applicate alla preparazione di dati testuali complessi, rumorosi o multilingua
    • D) metriche di valutazione dei modelli NLP, selezionate in funzione del task per verificare accuratezza, robustezza e affidabilità dei modelli
    • E) pratiche di integrazione e messa in produzione di modelli NLP, con attenzione agli aspetti di sicurezza, robustezza e mitigazione dei bias linguistici
Requisiti preferenziali
  • Conoscenza dei fondamenti di linguistica computazionale e semantica del linguaggio
  • Conoscenza delle problematiche di sicurezza e robustezza dei modelli NLP (es. data poisoning, prompt injection, attacchi adversariali)
  • Conoscenza dei framework e delle pratiche di governance e risk management applicabili ai sistemi AI basati su NLP
  • Buona conoscenza della lingua inglese in ambito professionale
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