Thanks to smart assistants such as Siri, Alexa and Cortana and writing tools like Grammarly, WhiteSmoke, and ProWritingAid, nowadays we are becoming more aware of how beneficial NLP gadgets can be in our daily lives!
What is NLP?
Natural Language Processing (NLP) is the ability of a computer programme to understand spoken and written human language. Over the past 50 years, NLP has been used in a variety of fields, including search engines, corporate intelligence, and medical research. For instance, it is used in the healthcare industry to expedite and enhance medical record searches, in the legal industry to assist in the study of essential legal documents, and in the financial industry to reduce the risk of fraud. NLP engineering can be the perfect career for you if you enjoy using your abilities to tackle new challenges and make a difference in the world!
Now, let us take a look at the responsibilities, skills, education, salary & job outlook of the NLP Engineering field.
What will be your responsibilities as an NLP Engineer?
As an NLP engineer, you will be in charge of creating and constructing text representation strategies as well as language comprehension systems. You’ll deal with speech patterns, artificial intelligence speech recognition, natural language comprehension, and natural language generation. You’ll break language down into simpler, more compact structures, try to comprehend how the forms relate to one another, and examine how the structural elements combine to convey meaning. Additionally, you will work on scripts to handle and analyse text data while gauging language comprehension precision.
Your duties will include:
- Designing and developing NLP systems per specifications
- Using efficient text representations to turn natural language into useful features
- Finding and using appropriate algorithms and tools for NLP tasks
- Training the resulting model
- Conducting evaluation trials
- Maintaining NLP libraries and frameworks
- Performing statistical analysis of data and model refinement
What skills are required to be an NLP Engineer?
A strong aptitude for mathematics, especially algebra and statistics, is a must for NLP engineers. You should:
- Be adept at engineering structures and data modelling
- Be passionate about creating something new each day
- Be exceptional communicators
- Flourish in situations of difficulty
- Stay current with emerging technologies
- Love working with others as a team
What are the Education requirements?
Although the level of schooling attained by NLP engineers varies, many hold degrees in Computer Science, Mathematics, or Statistics. Some people enter the workforce with a Bachelor’s degree, while others get their doctorate and develop their research techniques in academia before doing so. As NLP is becoming more popular across a wide range of businesses, you can identify your area of interest and get valuable experience in that area to land a position in that field.
What are the Salary Prospects?
- The average annual income for an NLP Engineer in India is roughly INR 7,30,000
- The average salary for an NLP engineer in the US is $1,20,000
- Your income potential can be significantly influenced by the industry and level of expertise
What is the Job Outlook?
In actuality, more than 2000+ job vacancies for Natural Language Processing Engineers are now listed on LinkedIn. Although this number will fluctuate every day, it is safe to assume that the job market for NLP engineers is promising. In contrast to other professions, the number of computer and information research scientists is predicted to increase by 22% between 2020 and 2030.
When it comes to technology and how people use it, Natural Language Processing is crucial. Many real-world applications, including chatbots, cybersecurity, search engines, and big data analytics, are employed in both the commercial and consumer sectors. NLP is projected to remain a significant component of business and daily life, despite difficulties.
NLP engineering is a constantly changing field, and you can steadily climb the ladder and reach higher ranks by virtue of your technical proficiency. For instance, you might decide to work as a full-stack developer, designing and creating software for both the front and back ends of apps. You can lead the tech and research teams in your company as a research scientist or manager if you have a passion for research.