5 Machine Learning Career Paths (In-demand and High Paying)

While training 10,000+ students and offering them the best placement assistance in machine learning, we have seen the use of data gaining popularity in small to large companies. 

What’s more important is training machines with authentic data and making them work without human intervention which requires knowledge of machine learning. 

This guide will give you an overview of what career opportunities you can explore in Machine Learning, along with the salary you can make and an idea for your career advancement roadmap.

Best Machine Learning Career Paths

Here are some of the possible career paths for a successful machine learning expert:

1. Machine Learning Engineer 

Machine learning engineers mainly focus on two roles: feeding machine learning models with preprocessed data and deploying the ML models in the production environment. They also build systems to automate predictive models and create AI algorithms that can learn and make predictions. 

Years of experience required: 2+ years

Job responsibilities: 

• You will be responsible to develop and implement machine learning algorithms and models
• These professionals must be experts in designing and creating different AI and deep learning systems
• You then perform data preprocessing and other feature engineering 
• Some MLE roles ask candidates to select appropriate datasets and data representation methods for machine training 
• You must know how to run machine learning tests and experiments to deploy high-quality models 
• Sometimes, ML engineers need to optimize existing machine learning pipelines and algorithms and suggest required changes
• They are responsible for creating new methods to improve AI on-device performance, model size, and accuracy
• You need to develop solutions to real-world problems through AI and ML with commercialization goals
• You need to work collaboratively with data scientists and software engineers and translate complex functional and technical requirements into detailed design

The image below illustrates the job requirements of a Machine Learning Engineer on LinkedIn

Key skills required: 

• You must have proficiency in programming languages like Python, or Java 
• This role requires experience in machine learning frameworks like TensorFlow, PyTorch, or Keras
• You require knowledge of big data technologies like Hadoop, Spark, or Dask and data streaming tools like Apache Flink, Apache Kafka, and Apache Storm
• Having experience with real-time ML systems and cloud platforms is good for ML engineers

Average annual salary: ₹10.6L in India, $1,65,221 in the US

2. AI (Artificial Intelligence) Specialist 

An artificial intelligence specialist is responsible for creating and implementing AI models and solutions, understanding the complexities of machine learning, deep learning and other AI technologies, and having knowledge to solve real-world problems. These professionals usually work closely with data scientists to ensure the use of high-quality data in AI models.

Years of experience required: 2+ years

Job responsibilities: 

• You are required to design, train, and implement machine learning models using frameworks like TensorFlow and/or PyTorch.
• AI specialists need to develop and manage preprocessing pipelines and machine learning models using the Python library Scikit-learn.
• You need to apply various supervised and unsupervised learning algorithms, such as SVM, Decision Trees, and Random Forest, to address complex problems.
• It is essential to learn how to design and implement deep learning architectures, including CNNs, RNNs, GANs, and transfer learning models, to push the boundaries of AI-driven solutions.
• Additionally, these specialists are required to integrate reinforcement learning techniques to enhance AI capabilities within the mobile coding environment.
• They are required to optimize existing AI models and make recommendations to improve performance and accuracy 
• These professionals also troubleshoot and debug issues in AI models for high-quality and reliable solutions 

We have shared the job responsibilities of an AI specialist role as shared on LinkedIn 

Key skills required: 

• You need proper experience in Python and machine learning frameworks.
• To become an AI specialist you must have certain experience in AI development and implementation
• These professionals must have the skills in using TensorFlow and/or PyTorch for model development and deployment.
• Additionally, you need strong expertise in Scikit-learn for building and implementing machine learning models.
• In-depth knowledge of both supervised and unsupervised learning algorithms and deep learning architectures is a must for AI specialists.

Average annual salary: ₹20.3LPA in India, $1,65,401 in the US

3. Computer Vision Engineers 

Computer vision engineers are experts who develop hardware and software for machines or computers to process visual data for solving real-world problems or performing specific tasks. These professionals use data from video feeds, digital signals, and analogue images digitized by the computer. 

Years of experience required: 2+ years

Job responsibilities: 

• You are responsible for developing algorithms that can enhance image quality in difficult conditions like low-light environments, noisy sensors, and adverse weather 
• These professionals need to select and implement different deep learning and machine learning models for computer vision tasks to complete object detection, stereo geometry, 3D reconstruction, etc 
• You will also train and improve models using datasets, and constantly evaluate their performance against relevant metrics for better output 
• These engineers need to pre-process and augment raw image and video data to improve the ML model quality and functioning 
• Then, you are required to document all the codes, and algorithms thoroughly and conduct unit testing to maintain a higher standard of code quality 
• These engineers often need to collaborate with cross-functional teams including software engineers, and data scientists to understand machine requirements and deliver effective solutions

The image below is a Computer Vision Engineer job post on LinkedIn that illustrates what a company is looking for in such professionals. 

Key skills required: 

• You must have knowledge of programming languages like Python or C++ for algorithm development and implementation 
• Familiarity with different libraries and frameworks related to computer vision like OpenCV it’s important for such engineers 
• You must also know machine learning and deep learning algorithms to train models and improve the accuracy of solutions 
• Additionally, the knowledge of image processing techniques like edge detection, filtering and feature extraction is essential for such a role 
• You must have a good grasp of mathematics and linear algebra to understand the underlying concepts of computer vision

Average annual salary: ₹10.4LPA in India, $1,66,324 in the US

4. Data Scientist 

Data scientists are professionals who find trends and patterns in large volumes of data. They use advanced analytics technologies, machine learning skills, and predictive modeling to collect, analyze, and interpret data and generate actionable insights, which company executives use to make important business decisions. These professionals also build data sets, discover correlations between them and convert data into a format that is understandable by a machine learning model. 

Years of experience required: 2+ 

Job responsibilities: 

• You need to identify business challenges and opportunities from company data for product/service improvements 
• The company also wants you to provide strategic recommendations by analyzing the data thoroughly 
• You need to apply data cleaning and wrangling expertise, quantitative analysis and determining to find patterns, trends and anomalies
• These professionals often need to collaborate with product and engineering teams to solve problems and identify hidden trends and opportunities 
• You need to build and train machine learning models to predict outcomes or make classifications and constantly evaluate their performance for better outcomes 
• Deploying these machine learning models into the production environment is also a task of data scientists along with monitoring key product metrics and understanding the root causes of any change in those metrics
• You must have good communication skills, visualization, and storytelling techniques to present your data findings and produce recommendations to companies clearly

Key skills required: 

• You must have knowledge of Python and an understanding of its libraries like NumPy, Pandas, Scikit-Learn 
• Get your knowledge of linear algebra, calculus, probability, and statistics clear for this role
• You need hands-on practice with data querying languages like SQL
• It is essential to learn how to handle missing data, outliers, feature engineering, and normalization
• You must have experience in machine learning types and algorithms and knowledge of neural network or Deep Learning algorithms
• Strong analytical skills of data scientists are essential to collecting, organizing, and analyzing large volumes of data 
• Data scientists need to be expert in creating and deploying machine-learning models
• You must have adequate knowledge of the key metrics to evaluate the performance of machine learning models

The image below illustrates the job responsibilities and skill requirements of a data scientist in the Machine Learning domain.

Average annual salary: ₹14.6L in India, $1,58,780 in the US

5. Computational Linguists 

Computational linguistics are experts who develop machines that deal with human languages. They have an understanding of both programming skills and linguistics and build systems that can perform multiple tasks like speech recognition, machine translation, speech synthesis, grammar checking, etc

Years of experience required: 2-7+ years

Job responsibilities: 

• They are responsible for developing and designing computers or machines that deal with human languages
• You need to build systems to perform tasks like speech recognition and text mining 
• Your responsibility also lies in creating and correcting data for language modeling, and building, testing and enhancing language models 
• These professionals need to create update and review phonetic transcriptions for speech dictionaries 
• You will also create systems to extract relevant content from databases and develop tools to support linguistic tasks 

Key skills required: 

• Computational linguists must have working knowledge of natural language data and natural language processing algorithms and tools 
• You require adequate knowledge of structural aspects of language like syntax, semantics and phonetics 
• Knowledge of any programming language like Python, Java and C++ is a must for linguists 
• You must also be familiar with object-oriented analysis and designing, and gain experience in ASR (Automatic Speech Recognition) and TTS (Text to Speech)
• Additionally, having strong mathematical skills and ability to analyze complex problems is a plus for computational linguists

We have shared the skill requirements for a computational linguistics job role from the LinkedIn job portal. 

Average annual salary: ₹6.0LPA in India, $1,25,878 in the US

Is Machine Learning a good career path in 2024?

Demand for machine learning skills is high

According to the US Bureau of Labor Statistics prediction, data scientist careers will grow by 36% and computer and information research scientist careers will rise by 21% from 2021 to 2031. Both these fields require machine learning engineers to source quality data and train machines to act like humans.

The search trend for “Machine Learning Engineer” has shown an upward rise in the past 5 years in India which means the domain is still in demand

Better job opportunities

Machine Learning experts can find lucrative job opportunities across multiple companies in India, especially after companies started using data to make their decisions. There are multiple other reasons why companies are investing in machine learning experts to develop systems that can perform certain tasks without human intervention. 

When I searched on LinkedIn for Machine Learning job roles in India, I found more than 16,000 opportunities across top companies like TCS, Amazon, Adobe, etc. 

Higher pay scale

If you go through different job opportunities in the Machine learning domain, you will find each of them offers competitive payments. However, in the initial years, the salary might not be as attractive as it gets when you have work experience of over 4-6 years. 

Higher opportunities for continuous learning 

There are many resources available on the Internet, like online courses, boot camps, certifications, and workshops, that can help you stay updated with machine learning concepts. We offer relevant online and offline courses for machine learning, various programming languages, MySQL, etc., that you can try. 

Machine Learning career opportunities in India

We have listed some of the common machine learning career opportunities in India.

Career path	Years of experience	Primary job responsibilities	Average annual salary in India
Machine Learning Engineer	1-5+	A machine-learning engineer’s primary responsibility is to design, develop, and deploy effective machine-learning models that solve real-world problems.	₹3.0LPA – ₹22.8LPA
Data Scientist	2-7+	The major responsibility of a data scientist is to derive insights from complex data, develop predictive models, and provide data-driven solutions to business problems that improve strategic decision-making.	₹3.8LPA – ₹28.0LPA
Computational linguists	1-3+	Computational linguists are responsible for designing and developing language-based computational systems, such as speech recognition, machine translation, and natural language processing, to enable effective human-computer interaction.	₹2.2LPA – ₹11.1LPA
AI Data Analysts	1+	An AI data analyst’s primary responsibility is to extract, clean, and analyze large datasets to uncover patterns and insights that can be used to improve AI models’ performance and decision-making.	₹1.0LPA – ₹6.0LPA
Computer Vision Engineer	2-7+	A computer vision engineer’s primary responsibility is to develop algorithms and systems that enable computers to understand and interpret visual information. They work on tasks such as image recognition, object detection, and image segmentation.	₹2.2LPA – ₹22.8LPA

What Do a Machine Learning Professional Do?

A machine learning professional develops and implements algorithms and models that enable machines to learn from accurate data and make intelligent decisions without much human intervention. They usually work as Machine Learning Engineers in the market. However, such professionals can diversify their careers into data science, business intelligence development, computer vision engineering, NLP engineering, AI development, etc. 

We have shared the roles and responsibilities of Machine Learning professionals in almost every field

• ML experts require data cleaning, preprocessing, and transforming them into suitable formats to ensure you feed reliable data to machines.
• They are responsible for designing, developing, and implementing ML algorithms as per the use cases or problems
• Professionals need to train models using labeled data and work on the model parameters to optimize performance
• You are then responsible for deploying the ML models into the production environment to produce real-time predictions and automate the decision-making process 
• They often need to collaborate with software engineers, DevOps teams, and other experts to integrate newer models into existing systems or develop new applications with better model integration 
• You need to regularly monitor the system and optimize them for better performance over time 

Skills and prerequisites to become a Machine Learning professional

Programming skills

Python is highly preferred for machine learning as it gives you the right tools and frameworks to train machines, analyze data, and solve complex problems, and its huge community can help you anytime. 

Start by learning the basics of Python programming including its syntax, and fundamental concepts.

You can then learn to use Pandas and NumPy for Exploratory Data Analysis and Matplotlib, Seaborn, and Plotly for data visualization. 

I found a question on Reddit, about whether it is data science or programming skills that are needed for the Machine Learning domain. Most of the comments to that post said both are equally important and some suggested that strong programming skills are essential to land a MLE (Machine Learning Engineer) job. Here’s the post for you

Knowledge of mathematics and statistics 

The main aim of Machine Learning is to train machines to learn independently without human intervention. Four concepts of mathematics, such as complex linear algebra, differential and integral calculus, probability, and statistics, are mainly needed in this area. Machine learning helps you design accurate predictive models that give insights based on data analysis.

Data engineering and pre-processing

To train machine learning models, you need to build data pipelines that can collect, store and prepare the data. This requires knowledge of data engineering and preprocessing of data. You need to learn data cleaning, transformation and integration of data, along with checking for missing values, feature engineering techniques, and data versioning. Data processing techniques clean and prepare the data before you feed them into the model. It includes learning different data scaling and normalization techniques and the knowledge of how to deal with categorical variables. 

Web scraping 

Now, you must know how to gather quality data and web scraping helps you fulfill that need. It refers to the process of extracting quality data from different websites using specific tools and library functions. Machine Learning experts must know web scraping to ensure they are feeding machines with accurate data. With us, you can learn web scraping using BeautifulSoup, to collect quality data and store them in appropriate formats like CSV and JSON. 

Learn Machine Learning techniques and algorithms 

One essential skill for Machine Learning Engineers is to know how to build accurate ML models. It requires a clear understanding of supervised, unsupervised, and reinforcement machine learning algorithms, and the process of implementing them. For example, Supervised learning is used to understand the relationship between the series of inputs and their corresponding output datasets. But, if you have inputs but no outputs and you want to identify patterns in the input, use unsupervised learning algorithms. 

Other concepts to learn in machine learning are data cleaning techniques, cross-validation techniques, hyperparameter tuning, feature engineering, model optimization techniques, model deployment, and exploratory data analysis.

Understanding of recommendation systems 

According to McKinsey insights, nearly 67% of consumers expect relevant service or product recommendations from brands. It makes companies invest in systems that can identify consumer preferences and offer them recommendations. Therefore, as a machine learning expert, you must understand these recommendation systems, mainly collaborative filtering and content-based filtering to help brands provide relevant recommendations to clients. 

Knowledge of reinforcement learning 

This is the part of machine learning that trains machines on trial-and-error methods to understand the best possible path they should consider in certain situations. You need to first learn the terminology of reinforcement learning, along with Markov Decision Processes, the Q-learning algorithm, and other concepts.

Competency in database management 

Machine learning experts must know database management systems to sort and manage large volumes of data and generate useful information from it. This includes learning SQL and NoSQL databases like MySQL, MongoDB, PostgreSQL, Redis, DynamoDB, etc. 

Working with cloud platforms

Cloud platforms like AWS, Azure, and Google Cloud provide services to build, train and deploy multiple machine learning models. For example, Sagemaker in AWS helps build high-quality, low-cost ML algorithms, 

Natural language processing for ML

Knowledge of natural language processing is essential for machine learning experts as it enables machines or computers to interpret, manipulate, and comprehend human languages. In NLP, you will learn different text preprocessing techniques including tokenization, stop word removal, and stemming, and fixed representation techniques like word embeddings and bag of words.

Machine Learning salary

Machine learning engineers’ average annual salary in India is ₹10.6L, while an entry-level engineer earns around ₹3.0LPA, a professional with up to 6 years of experience can earn about ₹22.8LPA which is really enticing for tech professionals looking for a highly-paid IT career opportunities in India. 

Top five cities in India	Average annual salary
Bangalore	₹3.1LPA – ₹25.0LPA
Hyderabad	₹3.0LPA – ₹20.0LPA
Vijayawada	₹2.8LPA – ₹12.7LPA
Pune	₹3.0LPA – ₹15.0LPA
Mumbai	₹3.5LPA – ₹15.0LPA

How Difficult is Machine Learning?

No, Machine Learning isn’t difficult if you have the passion and a rigid aim to constantly learn and practice ML concepts. The learning process can be a little challenging for you. Firstly, you have to learn multiple topics including a programming language, linear algebra and calculus, database management, and the use of cloud platforms, which is difficult. Secondly, there are multiple machine learning algorithms that can make you confused if not properly understood. However, if you know the prerequisites and get your foundation cleared before joining a Machine Learning course, you can easily become an expert in the field. 

Let me share with you a LinkedIn post in which a senior ML developer shares her story of learning Machine Learning herself. It proves that if you have passion for something, it isn’t difficult to achieve. 

Machine Learning course certification to start your career 

To start developing your career in the Machine Learning domain and land a high-paying job, you need to enrol for a Machine Learning certificate course from a reputed institution. But choosing the right course can be difficult especially if you are a beginner in this field.

At Codegnan, we have created a special machine-learning course for such beginners. It offers a practical approach to learning complex ML algorithms and related concepts. 

Our course starts with basic information about Machine Learning, its types, how to set up the development environment, and an overview of ML workflow. It also ensures that you know the machine learning syllabus beforehand, which will help you proceed with the entire course.

Upon completion of the course, you will receive an industry-accredited certificate that will help you land good jobs in India. 

Plus, we focus on providing hands-on experience to our learners so that they can implement their learning in real machines, face challenges and find solutions under the guidance of industry experts. 

We offer a one-month ML online and offline classes (Hyderabad and Vijayawada) at a special discounted price of ₹8000. 

Here’s our course outcome for Machine Learning students at Codegnan 

We have been rated 4.8 out of 5 on Google by 2391 people, which ensures you can trust us for your Machine Learning career goals. We assign top industry experts as your trainers, who know what is trending in the market and can prepare you for it in advance. 

FAQs 

Can a fresher get a job in Machine Learning?

Yes, a fresher can get a job in Machine Learning, but they might require experience in at least one programming language. Also, you need to enroll in a Machine Learning certificate course that provides adequate hands-on training on real-life projects. This ensures that, though you don’t have office work experience, you are ready for the industry.

Find our extensive list of machine learning projects.

What is the qualification for Machine Learning?

The qualification for Machine Learning students is usually a graduation degree in computer science, Mathematics, Statistics, and engineering. However, certain prerequisites are also required, such as good knowledge of a programming language, linear algebra, statistics, and calculus.

How can I become a Machine Learning expert after 12th?

Yes, you can become a Machine Learning expert after 12th grade if you have Mathematics and computer science as your main subjects. Then, enroll in a beginner course, which requires a 12th pass, and acquire an industry-accredited certificate to apply for internships or entry-level jobs.

Is Machine Learning still in demand in 2024?

Yes, Machine Learning is still in demand in 2024 and you can find thousands of opportunities for machine learning experts across multiple job boards and social networking sites. Some of them are also open to new experts, while most of them are for experienced ML professionals.