How to Become an NLP Engineer in 2024

How does Google find search results to show you what you need? Sentiment analysis is the key to natural language processing (NLP). NLP is used by devices such as Apple HomeKit, Google Assistant, and Alexa. With the advances in Generative AI last year, NLP has expanded greatly. According to Indeed, NLP engineer job posts have increased by more than 300% in the last five years. According to Fortune Business Insights, the global NLP industry is projected to expand at a 23.2% CAGR, from $29.71 billion in 2024 to $158.04 billion by 2032. 

Are you considering a career in NLP? Continue reading! We’ll go over career trends, how to become an NLP engineer, what skills you need, and wage differences between the US and India.

Table of Contents

What is NLP?
Who is an NLP Engineer?
NLP Engineer Job Description
Skills required to become an NLP Engineer
Step-by-Step Roadmap to Become an NLP Engineer
NLP Engineer Jobs Available
NLP Engineer Salary (India & USA)
How to Become an NLP Engineer: FAQs

Check out the video to get the insights on how to become NLP Engineer:

What is NLP?

Natural Language Processing, or NLP, is a subset of artificial intelligence that studies how computers interact with human language. It involves building models and algorithms that let computers read, comprehend, and produce text that is similar to that of a person. NLP is used in a variety of applications, such as voice recognition, chatbots, sentiment analysis, and language translation. 

For products like Google Assistant, Google Translate, and Google Search, Google uses natural language processing (NLP) in speech recognition, language translation, and search algorithms. Amazon uses NLP in Alexa to recognize voice commands, chatbots to assist customers, and product recommendations on their online store.

If you want to know more about ‘What is Natural Language Processing?’ you can go through this Natural Language Processing Using Python course!

Who is an NLP Engineer?

An NLP engineer is a technical expert who creates connections between computers and human language. They create and build the technologies that let computers comprehend, translate, and even produce human language.  NLP engineers are the minds behind technologies like chatbots, virtual assistants, and machine translation. 

NLP engineers often have a solid foundation in computer science, linguistics, and statistics. They need to know NLP frameworks (e.g., NLTK, SpaCy, TensorFlow, PyTorch), python programming, machine learning, and deep learning approaches.

NLP Engineer Job Description 

If you want to know the exact meaning of any given role, then you must read through several job descriptions for the same role. We examined the job postings from tech giants on various job boards such as LinkedIn to determine the duties of an NLP engineer. Here are some job descriptions from different companies.

Now let’s examine the roles and responsibilities of an NLP engineer, which can change depending on the projects they work on and the particular requirements of the company. However, the following are common tasks associated with this role:

• Create and build algorithms and models for processing and understanding natural language, such as named entity recognition, sentiment analysis, and language translation.
• Clean and preprocess large datasets to extract essential features, ensuring that NLP models are trained and tested with high-quality input.
• Train, fine-tune, and assess machine learning and deep learning models for natural language processing tasks with frameworks such as TensorFlow or PyTorch.
• Collaborate with cross-functional teams that include data scientists, software developers, and domain experts to integrate  NLP solutions into bigger systems.

For an NLP engineer to effectively contribute to the development and use of NLP solutions, a combination of technical expertise, domain knowledge, and problem-solving skills is needed. Now let’s examine the skills required to become an NLP engineer.

Skills required to become an NLP Engineer

Here is a detailed table that lists the necessary technical and soft skills required to become an NLP (Natural Language Processing) engineer:

To become a successful NLP Engineer, you must have both technical expertise and soft skills, as well as a well-structured step-by-step roadmap. Let’s now examine a detailed roadmap for becoming an NLP engineer.

Interested in learning Artificial Intelligence? Go through this Artificial Intelligence Tutorial!

Step-by-Step Roadmap to Become an NLP Engineer

Step 1: Familiarize with Programming Languages

Start with Python as the primary programming language for your NLP tasks. It is popular among developers because of its simplicity, readability, and extensive library support, which includes TensorFlow, Keras, and NLTK. However, other languages like R, Java, or Scala can also be considered as an option.

Make sure you understand the basics of object-oriented programming, control flow, syntax, and data structures. Next, get started using NumPy, Pandas, Scikit-learn, and other tools. If you’re a beginner, there are a ton of online lessons and courses easily available.

Step 2: Go through Mathematical Concepts

Working with NLP models such as word embeddings requires a solid understanding of ideas from linear algebra, including matrices, vectors, and operations like matrix multiplication and dot product. Proceed to Calculus: Differentiation and Integration. And study statistics and probability, including ideas like statistical analysis, hypothesis testing, and probability distributions.

Step 3: Machine Learning Concepts and ML Libraries

Get familiar with fundamental machine learning concepts such as supervised and unsupervised learning, model evaluation metrics, and popular algorithms like decision trees, k-nearest neighbors, and linear regression. Subsequently, consider acquiring knowledge of well-known machine learning libraries such as sci-kit-learn  in Python, which offers tools for data preprocessing, model training, and evaluation along with implemented machine learning algorithms.

Start with understanding supervised learning concepts  as classification or regression and unsupervised learning concepts like clustering and association. Learn about popular machine learning algorithms  like Linear Regression, Logistic Regression, SVM, Decision Tree, k-nearest Neighbour, Random Forest, etc. Then, go on learning with ML libraries like NumPy, Pandas, Scikit-learn, OpenCV, etc.

Step 4: Deep Learning Concepts and Libraries

NLP models frequently rely on deep learning as their foundation. Recognize the diverse network topologies, such as recurrent and convolutional neural networks (RNNs), activation functions, and backpropagation, as well as the structure and operation of neural networks.

Learn to use deep learning libraries, such as PyTorch or TensorFlow, which are popular for creating and refining deep learning models. These libraries provide tools for managing computations, designing neural network designs, and optimizing model parameters.

Step 5: Mastery in Data Science Fundamentals

Gain mastery in the fields of exploratory data visualization, data cleansing, and data preprocessing. Learn how to prepare textual data for NLP models by handling missing values, normalizing, encoding categorical characteristics, and cleaning the text (such as tokenization, stemming, and lemmatization). Gain knowledge of patterns and correlations through data exploration and visualization. These abilities can help with feature engineering and model interpretation.

Start by being familiar with some tools, such as Tableau, Power BI, or Python libraries, such as Matplotlib and Seaborn. You can clean and visualize data with the aid of these tools.

Step 6: Deep Dive into NLP Concepts and Libraries

To begin with, familiarize yourself with fundamental NLP principles such as tokenization, stemming, lemmatization, named entity identification, part-of-speech tagging, and sentiment analysis. Next, investigate NLP libraries such as spaCy or NLTK, which offer features specific to NLP applications. Using tutorials and sample projects, put these principles into practice.

Step 7: Take on Personal and Open-source projects

With the knowledge you have gained, put it to use by working on personal projects such as creating a chatbot, text summarizing software, or sentiment analysis tool. You may also contribute to open-source projects by taking part in NLP Projects across several platforms. You get to work with other developers on real-world codebases and have the chance to pick the brains of seasoned contributors. This gives you the chance to try new things, get practice working with actual data, and build a portfolio of your work.

I’ve included a list of some well-known platforms below if you’re searching for some to start your open-source contributing journey:

• GitHub – The most well-known site for hosting open-source projects, with features including pull requests, code collaboration, version control, and problem tracking.
• Kaggle – A community platform for data science and machine learning discovery and collaboration that includes datasets, contests, and tools.
• Quine – This platform focuses on linking developers with open-source projects that require contributions. Based on their expertise and experience, they connect developers with projects via data analysis.

Step 8: Look for Internships and Freelance Work 

Now that you gained the necessary skills and concluded your projects, search for internship opportunities with organizations that work on NLP projects, or think about taking on freelancing work on sites like Fiverr or Upwork. By doing this, you can expand your portfolio and gain expertise collaborating with clients on actual NLP jobs. It also assists you in expanding your network with industry professionals and other like-minded individuals.

Get ready for high-paying NLP jobs with these Top NLP Interview Questions and Answers!

NLP Engineer Jobs Available 

Discussing recent job trends, a report by Statista projects the market size of the Natural Language Processing (NLP) market to reach US$29.19 billion by 2024 and US$63.37 billion by 2030, indicating an annual growth rate (CAGR 2024-2030) of 13.79%.

Right now, if you go over to the search engine and search for NLP engineer jobs available in India, you will find out that on just the LinkedIn job board there are 3,000+ jobs available. Whereas if you search for data analytics jobs in the USA, you will find out that there are 15,000+ jobs available. 

And that’s just LinkedIn. The numbers would be significantly higher if one included other job boards like Indeed or Naukri.com. For this reason, you have to learn how to become an NLP Engineer and get ready as soon as possible!

NLP Engineer Salary (India & USA) 

Let’s have a look at the salaries for NLP engineers in India and the United States. The table below clearly summarizes the compensation details received from Glassdoor.

The compensation for NLP engineers might differ depending on several  criteria, including region, experience level, and the hiring organization. These numbers are just combined averages. Salaries in India can reach up to ₹20 lakhs per year, while in the US they can go up to US$ 228k per annum.
Know more about ‘What is Natural Language Processing?’ and clear your doubts and queries from our experts in our Artificial Intelligence Community!

How to Become an NLP Engineer: FAQs