AI Jobs for Freshers : How to get a Job in AI as a Fresher

AI Jobs for Freshers : How to get a Job in AI as a Fresher


Artificial Intelligence (AI) is a rapidly evolving field with the power to transform numerous sectors, including healthcare, transportation, entertainment, and finance, to mention a few. With more companies now exploring AI-based solutions for their operations, the demand for professionals with the right AI skills has seen a meteoric rise. Fresh graduates, in particular, can find numerous opportunities in this dynamic field, provided they have the right skills and knowledge. In this article, we will dive into the various job roles for freshers in AI, the required skills, salary trends, and tips to break into the industry.

AI Job Roles for Freshers

AI Data Analyst

An AI Data Analyst plays a crucial role in any organization that leverages AI. Their primary responsibilities include collecting, processing, and performing statistical analyses on large sets of data. Their aim is to sift through massive amounts of data and identify trends and patterns that can be used to make business decisions.

To achieve this, they develop data-driven solutions that utilize machine learning and AI technologies. A basic understanding of data structures and algorithms is a key requirement for this role. They should be proficient in programming languages like Python or R, which are commonly used for data analysis.

Data Analysts often work closely with other teams in the organization, such as the marketing or sales departments, to understand their data requirements and deliver meaningful insights. They also need to present their findings in a manner that is understandable to non-technical stakeholders.

Machine Learning Engineer

Machine Learning Engineers are at the heart of AI projects. They are involved in creating and implementing machine learning models to solve complex business problems. This role provides freshers with a challenging yet rewarding experience, as it requires working with cutting-edge technologies and applying them in real-world scenarios.

Machine Learning Engineers should be familiar with a variety of programming languages including Python, Java, or C++. Knowledge of libraries like TensorFlow and PyTorch, used for creating machine learning models, is also crucial for this role.

A Machine Learning Engineer works closely with Data Scientists and AI Researchers to build and optimize models. The ability to interpret and visualize data, along with a strong understanding of machine learning algorithms and principles, is a must.

AI Engineer

AI Engineers are responsible for designing and implementing AI models. This involves developing prototypes, setting up AI workflows, and integrating AI with other technologies to create comprehensive solutions. They also play a significant role in maintaining the AI infrastructure in an organization, ensuring that it meets the necessary requirements and stays up-to-date with the latest AI advancements.

Handling large-scale AI projects is often part of an AI Engineer’s role. Therefore, they need to possess a strong background in machine learning, deep learning, and programming languages. They should also be proficient in cloud platforms and have a good understanding of hardware requirements for AI applications.

AI Engineers often have to collaborate with various teams within an organization, including data scientists, business analysts, and IT teams, to ensure successful implementation of AI projects.

Business Intelligence Developer

Business Intelligence Developers play a crucial role in utilizing data to help businesses make more informed decisions. They take raw data and transform it into meaningful information that can drive strategic decision-making. They do this by creating and maintaining BI interfaces, such as dashboards and data visualization tools, which provide a clear view of the business metrics.

In the realm of AI, Business Intelligence Developers are familiar with machine learning techniques and often apply them to derive insights from data. They use these insights to identify business opportunities, improve efficiency, or even predict future trends.

Communication is a key skill for a Business Intelligence Developer as they often need to present their findings to stakeholders. They should be able to translate complex findings into understandable insights for the company. Familiarity with BI tools like Tableau, Power BI, and programming languages such as SQL, Python, or R is highly beneficial for this role.

Skills Required for AI Jobs

The field of Artificial Intelligence (AI) is vast and complex, requiring a unique blend of skills to navigate successfully. For freshers looking to venture into AI, here are the key skills you should focus on:

Programming Skills

Programming is the backbone of any AI job. Languages such as Python, R, and Java are frequently used in AI and machine learning. Python, in particular, is widely used due to its simplicity, flexibility, and the availability of numerous libraries and frameworks that are useful in AI, such as TensorFlow, Keras, and PyTorch.

R is popular in statistical computing and graphics, making it useful for data analysis and visualizations. Java, on the other hand, is a versatile language used in various AI applications, including natural language processing and search algorithms. Having a solid grasp of at least one of these languages is essential for a career in AI.

Statistics and Probability

AI, at its core, is about making predictions – and predictions are grounded in statistics and probability. A strong understanding of these subjects is crucial to creating effective machine learning algorithms. Concepts such as Bayesian probability, mean, median, standard deviation, and correlation are often used in data analysis and model training. Understanding these can help in choosing the right model for your data and evaluating its performance accurately.

Data Modeling and Evaluation

Data modeling is the process of creating a structured representation of data. This skill involves defining how different data elements relate and interact with one another. It’s a critical step in preparing your data for machine learning.

Evaluation, on the other hand, involves assessing the performance of your machine learning model. This often involves splitting your data into training and testing sets, training your model, and then testing it to evaluate its performance. Being able to accurately model and evaluate data is key to creating effective AI solutions.

Understanding of Machine Learning Algorithms

A foundational understanding of various machine learning algorithms and the mathematics behind them is a must. This includes linear and logistic regression, decision trees, clustering algorithms, neural networks, and more. Understanding these algorithms helps in selecting the most appropriate one for your specific problem and data set.

Advanced Signal Processing Techniques

Signal processing techniques are used to analyze, modify, and synthesize signals such as sound, images, and scientific measurements. In the context of AI, these techniques are useful for roles that involve creating algorithms that can learn from and make predictions on data. For example, signal processing techniques are often used in speech recognition, image recognition, and natural language processing.

Natural Language Processing (NLP)

NLP is a field of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to enable computers to understand, interpret, and generate human language in a valuable way.

This skill is increasingly important as businesses are using AI to analyze text data, build chatbots, and create personal assistants. A solid understanding of NLP techniques such as tokenization, named entity recognition, and sentiment analysis is valuable in many AI roles.

The field of Artificial Intelligence (AI) is one of the most lucrative career paths, offering competitive compensation packages even at the entry level. However, the exact compensation can vary significantly depending on factors such as job role, geographical location, the level of skills, and the organization’s size and industry.

Different roles in AI command different salaries. For instance, roles such as Machine Learning Engineers or AI Engineers often fetch higher salaries due to the high demand and the technical expertise required. On the other hand, roles such as Data Analysts or Business Intelligence Developers may offer slightly lower salaries but still considerably higher than many other technology jobs.

The geographical location also significantly impacts AI salaries. For instance, countries with a high cost of living such as the United States, Australia, and the United Kingdom generally offer higher salaries compared to countries with a lower cost of living. However, the competition for AI roles is also higher in these countries.

In the United States, the average entry-level salary for an AI Engineer was $114,000 per year in 2022, according to Glassdoor. This figure may be significantly higher in tech hubs like San Francisco or New York, where the demand for AI professionals is high.

The level of skill and expertise a fresher has can greatly influence their starting salary in an AI role. Those with a robust portfolio demonstrating advanced skills and hands-on experience with real-world projects can command higher salaries compared to those with only theoretical knowledge.

The industry employing AI can also influence salary trends. For instance, tech giants like Google, Amazon, or Facebook are known to offer high salaries for AI roles due to the high value they place on AI technologies. On the other hand, startups or smaller companies may offer lower salaries but might provide equity or other benefits.

To summarise, whilst AI offers some of the highest-paying jobs in the tech industry, the actual salary can vary based on several factors. Nevertheless, freshers can expect attractive compensation that is likely to increase as they gain more experience and skills in the field.

Breaking into AI as a Fresher

Embarking on a career in AI can seem challenging due to the field’s complexity and the extensive skillset it requires. However, with the right approach and resources, freshers can successfully navigate their entry into this promising sector.

Acquire Relevant Skills

Acquiring relevant skills is the first step to embarking on a career in AI. As outlined earlier, certain technical skills, such as proficiency in programming languages like Python, R, and Java, understanding of machine learning algorithms, and familiarity with concepts of statistics and probability, are vital for AI jobs.

There are numerous online platforms that offer courses catering to various AI disciplines. Websites such as Coursera, edX, and Udemy offer comprehensive courses that provide theoretical knowledge and practical applications. These courses are often taught by experts in the field and offer certificates upon completion, adding significant value to your resume.

Build a Strong Portfolio

Alongside acquiring skills, building a robust portfolio is crucial to demonstrate your competency in AI. This portfolio should include projects that showcase your understanding of AI concepts, your programming skills, and your ability to solve complex problems.

Projects could range from simple ones, like a predictive model using linear regression, to more complex ones, such as building a chatbot using Natural Language Processing (NLP). Make sure to document these projects thoroughly, explaining the problem, your approach, and the solution. Hosting your projects on platforms like GitHub can also give potential employers direct access to your work.


Networking is an often overlooked yet essential aspect of starting a career in AI. Being part of AI communities can provide you with exposure to industry trends, offer insights from experienced professionals, and open doors to potential job opportunities.

You can join online communities such as forums on Reddit, LinkedIn groups, or Slack channels dedicated to AI. Additionally, attending workshops, webinars, and conferences can enhance your understanding of the field and allow you to connect with professionals who can offer guidance and mentorship.

Entering the AI industry as a fresher might seem daunting, but by acquiring the necessary skills, showcasing your knowledge through a solid portfolio, and actively networking, you can successfully launch your AI career. Remember, it’s a field that’s constantly evolving, so continuous learning and adaptability are keys to success.


The future of AI is bright, and so are the opportunities for freshers. The key to making it in the industry is to stay up-to-date with the latest technologies, continuously improve your skills, and have a passion for learning. With the right blend of technical and soft skills, a career in AI can be immensely rewarding, both intellectually and financially.

Online Resources and References

  1. Towards Data Science: An online platform that provides high-quality content on data science, machine learning, and AI. They also offer numerous articles helpful for freshers.
  2. AI Hub – Google Developers: A repository of plug-and-play AI components, including datasets, TensorFlow models, and more. A great place for freshers to get hands-on experience.
  3. Glassdoor: A website where employees and former employees anonymously review companies and their management. It also provides job listings and salary information, helpful for freshers to understand the job market.
  4. AI Stack Exchange: A question and answer site for professionals, academics, and students in AI. This is a great platform for freshers to get their doubts clarified and gain more insight into the field.
  5. GitHub: A platform for version control and collaboration, where people can work together on projects. Freshers can find numerous AI projects to work on or draw inspiration from.