In 2025, businesses aren’t just analyzing data; they’re understanding language. From customer support chatbots to AI-powered document summarization and voice assistants that actually get you, the magic behind it all is natural language processing (NLP). And the experts making it happen? NLP engineers.
Hiring an NLP engineer today is all about gaining a competitive edge in a language-first digital world. As companies race to integrate AI into their platforms, demand for professionals who can bridge the gap between human language and machine learning has skyrocketed.
So, what exactly does an NLP engineer do? What technical skills should you look for? And how much should you expect to pay, especially if you're considering hiring remote or nearshore NLP talent from places like Latin America?
In this guide, we’ll break down the NLP engineer role, highlight the most in-demand skills in 2025, compare salary expectations across regions, and help you understand when and where to find top NLP talent for your growing AI team.
What Does an NLP Engineer Do?
An NLP engineer is the language architect behind intelligent systems that understand, interpret, and generate human language. They design algorithms that help machines read between the lines, whether it’s detecting sarcasm in a tweet, pulling insights from legal documents, or powering voice search in your favorite app.
At a high level, NLP engineers sit at the intersection of machine learning, linguistics, and software engineering. Their job is to teach machines how to process language the way humans do: messy grammar, slang, ambiguity, and all.
Here’s what a natural language processing engineer typically works on:
- Training and fine-tuning language models (like BERT, GPT, or custom LLMs)
- Text classification for things like spam detection, sentiment analysis, or intent recognition
- Entity recognition to extract names, dates, and key phrases from unstructured data
- Machine translation for multilingual platforms
- Building conversational agents like chatbots and virtual assistants
- Information retrieval and search optimization based on user queries
In practical terms, NLP engineers help businesses turn unstructured text like emails, reviews, transcriptions, and social media comments into actionable insights and automated experiences. That makes them essential for companies building smarter customer interactions, content automation tools, or data-driven decision engines.
Whether you're working with massive language models or niche AI applications, having a skilled NLP developer on your team can unlock entirely new capabilities for your product or service.
Key Skills to Look for in an NLP Engineer
Not all AI professionals are built the same, and when it comes to natural language processing, the right combination of technical chops and linguistic intuition can make or break your project. If you're looking to hire an NLP engineer in 2025, knowing which skills to prioritize will help you identify the talent that can deliver results from day one.
Here’s what to look for in a top-tier natural language processing engineer:
Strong Programming Foundations
Look for fluency in Python (a must-have in the NLP world) as well as experience with libraries like NumPy, pandas, and scikit-learn. Bonus points if they can handle Java, C++, or R for specific enterprise needs.
Mastery of NLP Libraries and Frameworks
Your ideal candidate should know their way around industry-standard tools like spaCy, NLTK, Hugging Face Transformers, Stanford NLP, and OpenNLP. These are essential for tasks like tokenization, parsing, and semantic analysis.
Machine Learning and Deep Learning Knowledge
They should understand key ML concepts (regression, classification, clustering) and be familiar with deep learning frameworks like TensorFlow, PyTorch, or Keras, especially for building and fine-tuning large language models (LLMs).
Understanding of Linguistics and Language Structures
An effective NLP engineer understands how language works (morphology, syntax, semantics, and pragmatics). This background allows them to design smarter algorithms for tasks like entity recognition or context-based classification.
Experience with Model Evaluation and Deployment
It’s not just about building models; it’s about getting them into production. Look for skills in model evaluation (precision, recall, F1 score), MLOps, and working knowledge of cloud platforms like AWS, GCP, or Azure.
Familiarity with Transformers and LLMs
In 2025, working knowledge of pre-trained transformer models like BERT, RoBERTa, GPT, and their fine-tuning methods is essential. Many businesses now expect NLP engineers to plug into or customize LLM pipelines.
Data Privacy and Ethical AI Awareness
With AI under scrutiny, it’s increasingly valuable to hire engineers who understand bias in language models, data anonymization, and ethical considerations when building NLP systems.
Hiring someone with this mix of skills ensures your company doesn’t just implement NLP; it does so effectively, responsibly, and at scale.
Must-Have Tools and Technologies
To build high-performing NLP systems in 2025, engineers need more than just theoretical knowledge; they need hands-on experience with the right tools and platforms. If you're hiring an NLP engineer, evaluating their familiarity with current technologies can help you identify candidates who are ready to contribute immediately.
Here are the must-have tools and technologies your NLP engineer should be comfortable with:
NLP Libraries and Toolkits
Look for expertise in popular libraries that power most modern NLP pipelines:
- spaCy – for fast, production-grade NLP tasks
- NLTK (Natural Language Toolkit) – for research and academic-style preprocessing
- Hugging Face Transformers – for leveraging and fine-tuning pre-trained language models
- Stanford CoreNLP, OpenNLP, and Gensim – for parsing, dependency trees, and topic modeling
Deep Learning Frameworks
NLP engineers often build custom models using:
- TensorFlow
- PyTorch
- Keras
These are essential for developing neural network architectures such as RNNs, CNNs, and transformers.
Data Processing and Visualization Tools
Strong candidates should also be proficient in:
- pandas and NumPy – for data manipulation
- matplotlib, Seaborn, or Plotly – for visualizing training metrics, word distributions, and embeddings
Model Deployment & APIs
A great NLP engineer doesn’t just train models; they deploy them:
- Flask or FastAPI – for wrapping models in APIs
- Docker – for containerizing NLP applications
- MLflow or Weights & Biases – for model tracking and experimentation
Cloud Platforms and MLOps
Experience with AWS, Google Cloud Platform, or Azure is crucial for scaling models and managing NLP workloads in production. Look for familiarity with:
- SageMaker
- Vertex AI
- Azure ML
Version Control and Collaboration
Proficiency in Git, GitHub, and CI/CD pipelines is key for team-based development, especially when collaborating on large AI systems.
When you're building language-powered features or automating text-heavy workflows, hiring someone with this technical toolkit ensures that your NLP engineer is both productive and future-proof.
NLP Engineer Salary in 2025: U.S. vs. Latin America
As the demand for NLP engineers continues to grow, so do the salary expectations. In 2025, natural language processing engineers command competitive pay, especially in the U.S., where AI roles are among the highest-paid in tech.
But for companies looking to build or scale AI teams efficiently, nearshoring NLP talent from Latin America offers a smart, cost-effective alternative without sacrificing quality.
Here’s what you need to know about NLP engineer salary trends this year:
Average U.S. Salaries for NLP Engineers
- Junior-Level (0–2 years): $95,000 – $120,000
- Mid-Level (2–5 years): $120,000 – $150,000
- Senior-Level (5+ years): $150,000 – $200,000+
These ranges reflect base salary and don’t include bonuses, equity, or relocation packages, which are common in enterprise AI roles.
Average NLP Engineer Salaries in Latin America (Remote Roles)
- Junior-Level: $25,000 – $35,000
- Mid-Level: $35,000 – $55,000
- Senior-Level: $55,000 – $80,000
NLP engineers in countries like Argentina, Brazil, Mexico, and Colombia are highly educated, often fluent in English, and increasingly experienced in cutting-edge AI tools like Transformers and LLMs.
Cost Savings and Talent Quality
Hiring remote NLP engineers from Latin America can result in up to 60–70% in salary savings compared to U.S. hires, without the time zone friction often experienced with teams in Asia or Eastern Europe.
Companies also benefit from:
- Strong STEM education in Latin American countries
- Cultural compatibility and real-time collaboration
- Fast-growing AI communities and developer ecosystems
Here’s a visual breakdown of salary ranges:
Whether you're hiring your first NLP engineer or expanding your AI team, understanding these salary benchmarks will help you budget wisely and stay competitive in the global talent market.
When Should You Hire an NLP Engineer?
Not every company needs an NLP engineer on day one, but when the time is right, bringing one on board can transform how your business understands and interacts with data, customers, and content. So, how do you know when it's time to hire an NLP engineer?
Here are key signs your company is ready:
You’re drowning in unstructured text data.
If your business relies on large volumes of emails, support tickets, survey responses, social media comments, or legal documents, you need more than keyword searches. An NLP engineer can help extract meaningful insights automatically.
You’re building or scaling an AI-driven product.
Launching a chatbot, voice assistant, semantic search engine, or recommendation system? These aren’t just plug-and-play features; they require a deep understanding of language context, which is exactly what NLP engineers specialize in.
You're using LLMs or fine-tuning transformer models.
Fine-tuning GPT-based models, building custom pipelines with BERT, or creating industry-specific LLM applications? Then you’ll need a specialist who knows how to handle tokenization, embeddings, attention mechanisms, and data preprocessing.
You want to automate language-heavy processes.
Manual document classification, language translation, contract summarization, and sentiment analysis can all be automated with NLP. A skilled engineer can design systems that cut down costs and improve accuracy.
Your customer experience depends on language understanding.
If you're scaling customer support, building multilingual apps, or optimizing content for global users, NLP is no longer optional. It’s foundational.
You’re investing in long-term AI capabilities.
Hiring an NLP engineer now sets the stage for scalable, AI-powered systems later. The sooner you integrate language expertise into your engineering team, the stronger your product roadmap becomes.
Where to Find and Hire Top NLP Engineers
Once you’ve decided it’s time to hire an NLP engineer, the next step is figuring out where to find the right talent and how to hire them efficiently. Whether you're looking for in-house engineers or remote experts, there are more options than ever in 2025.
Here’s how to approach your search:
Tap Into Remote Talent in Latin America
For U.S. companies, hiring NLP engineers in Latin America has become a go-to strategy. Why? You get highly skilled talent in your time zone, often at a fraction of U.S. salaries. Countries like Argentina, Colombia, Brazil, and Mexico have strong AI education programs and growing NLP communities.
That’s where South can give you a hand. South specializes in helping U.S. businesses find, hire, and manage top NLP engineers and other AI talent across Latin America. All candidates are pre-vetted, English-proficient, and ready to work full-time remotely; no freelance churn, no compliance headaches.
Why hire through South?
- Access to elite NLP engineers in your time zone
- Streamlined sourcing, vetting, and hiring
- Local payroll and compliance handled for you
- Transparent pricing and fast hiring cycles
If you're serious about scaling your AI team without inflating your budget, South is the partner to help you do it right.
Use Trusted Recruitment Platforms and Agencies
Looking for pre-vetted candidates? Consider specialized platforms or AI-focused recruitment firms that know how to source and screen NLP talent.
Top options include:
- Toptal – for elite freelance NLP engineers
- Turing – for remote AI and ML developers
- Upwork or Fiverr Pro – for short-term NLP consultants or tasks
- Wellfound (formerly AngelList Talent) – for startups building AI teams
Explore Technical Communities and AI Conferences
Active contributors in the NLP space often share their work publicly. Search on:
- GitHub – look for contributors to open-source NLP projects
- Kaggle – where NLP competitions showcase top talent
- LinkedIn – filter by NLP skills and LLM experience
- Conferences like ACL, EMNLP, and NeurIPS – many publish speaker lists and open-source research
Evaluate More Than Just Résumés
NLP is a specialized field. Beyond checking for keywords, look for:
- Hands-on projects using transformers, BERT/GPT models
- Contributions to NLP libraries or datasets
- Strong communication skills (yes, ironically crucial for NLP engineers)
- Experience deploying NLP models into real-world applications
Hiring right doesn’t mean hiring fast; it means hiring someone who understands both the language and the tech behind it. With the right sourcing strategy, you can build a smart, scalable team without overextending your budget.
The Takeaway
As language-driven AI becomes central to how companies operate, communicate, and scale, NLP engineers have moved from a niche hire to a strategic one. Whether you're building chatbots, fine-tuning large language models, or automating document workflows, having the right NLP engineer on your team can unlock new levels of speed, insight, and customer experience.
And in 2025, you don’t have to choose between top talent and cost-efficiency. By hiring NLP engineers remotely from Latin America, you gain access to world-class expertise, without the Silicon Valley price tag or timezone friction.
If you’re ready to build a smarter, more scalable AI team, South is here to help. We connect U.S. companies with top NLP engineers and AI professionals from across Latin America; pre-vetted, fully remote, and ready to contribute from day one.
Want to meet your next NLP engineer? Schedule a free call today to find AI talent that works in your time zone!
