AI Developer Vs Data Scientist: Which Career Path is Right for You in 2025?

Among the millions of jobs that computer science has created so far, data scientists and AI developers are the two most prominent names dominating the technical landscape. A report indicates that AI-based jobs are expected to exceed 2.3 million by 2027. This might create a talent shortage for AI professionals, making it a highly desirable career option for students and working professionals considering a career switch. 

If you are exploring future-ready careers like AI Developer or Data Scientist, Amity Online offers the right programs for you. To build a career in AI, you can pursue the Online MCA in AI & ML degree course, while aspiring Data Scientists can opt for the Online MBA in Data Science to gain expert skills and career growth.

Keep reading to learn more about AI developer vs data scientist roles, their salaries, career options, and the best tech career in 2025.

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What Does an AI Developer Do?

An AI developer designs and deploys AI-powered tools that mimic human intelligence. These tools execute tasks that would otherwise require a human workforce. The best example is ChatGPT. A user can write a prompt and run it to get real-time answers. From images to real-time updates, ChatGPT can handle it all with ease.

Simply put, they train models to perform tasks that earlier required human capabilities. From translating languages to teaching how the stock market works to interpreting X-rays and other tests, AI tools can do it all within seconds. 

Key Responsibilities of an AI Developer

An AI developer creates models that can learn and improve with time. Here's what their primary job responsibilities include:

• Gathering a massive amount of raw data 
• Building models
• Feeding data to the AI model 
• Training the model not just to use the memorised data but also to adapt to new data.
• Integrating the AI tool into a website or a software application to test its performance in a real-world application

Let's say an AI developer has deployed an AI chatbot for a stock brokerage website. Their day starts with checking the chatbot's performance. They check how the AI tool responds to users' questions and how many people are satisfied with the chatbot's assistance. They also look for areas where the chatbot struggles or does not deliver the expected performance.

​For instance, if the chatbot couldn't answer vague questions related to the stockbroker account setup, the data scientist could change the way the chatbot collects information from the website and presents it to the user.

Career Options

Here are a few popular AI career paths for aspiring developers:

1. Natural Language Processing Engineer: 

An NLP engineer is trained to develop intelligent systems that can read, write, and talk. These models can mimic human functions, such as translation and sentiment analysis. The average annual salary of an NLP developer is ₹3.0 Lakhs to ₹25.5 Lakhs per year.

2. Robotics Engineer: 

A robotics engineer programs robots to conduct physical tasks with better precision. They integrate machine learning capabilities into these robots to streamline human jobs. A notable example is the robotic arm used in robot-assisted surgical procedures, which enhances accuracy and minimises the risk of complications. A robotics engineer can earn up to ₹9.6 Lakhs per year. 

3. AI Software Developer: 

The AI software developer can build robust AI models that can make decisions, solve problems, and analyse data. They also integrate these tools with traditional software applications to enhance their function. The average annual AI developer salary is ₹3.5 Lakhs to ₹27.0 Lakhs per year.

Also Read:- How Students Use AI for Exams & Career Planning

What Does a Data Scientist Do?

Machine learning vs data science is a common discussion topic. While they may be used interchangeably, they have different meanings. A data scientist uses the company's extensive datasets to predict what might happen and how an organisation can stay ahead of the trend, while machine learning focuses on building models that learn from data.

A data scientist utilises mathematical skills, analytics, artificial intelligence, and machine learning to collect raw data and derive actionable insights. 

Key Responsibilities of a Data Scientist

The first part of the job involves collecting data from various sources, including websites, social media platforms, surveys, and sensors. Data scientists prepare the data by identifying missing values, errors, and duplicate entries. 

They use graphs, statistics, and charts to analyse data. For instance, a data scientist working at an e-commerce company is tasked with determining the reason for a decline in sales over the last three months. They will examine the traffic, bounce rate, abandoned carts, customer engagement, and other key metrics to identify the reason and communicate their findings to the team lead.

Career Options

The data scientist's job scope has gone beyond data engineering. Here are some trending data scientist jobs:

1. Data Analyst: 

An analyst cleans the data to prepare it for analysis and interpretation. They must be proficient in SQL, Python, and Excel. Although it's an entry-level role, the salary of a data analyst can range from ₹13.8 Lakhs per year.

2. Machine Learning Engineer: 

An ML system collects data and creates a model that learns from the data, improving over time with human interactions. The average salary of an ML engineer ranges from ₹3.5 Lakhs to ₹24.7 Lakhs.

3. Data Engineer: 

A data engineer works in the backend to make data accessible to the analysis team. Their job is to collect data from different sources, organise it, and store it. The salary of a data engineer can range from ₹22.0 Lakhs per year. 

AI Developer Vs Data Scientist: Which is Better?

Both AI developers and data scientists have a broad scope of responsibilities. They open doors to many rewarding career options. The most suitable career choice for an individual depends on their passion, salary expectations, and future goals. 

Amity Online offers advanced courses that prepare students for both fields. Students can browse the website to check relevant programmes and sign up for a course that best fits their requirements.

Final Words

Students interested in building intelligent systems or integrating AI into real-world applications can pursue a course in it. Those who enjoy working with data and possess exceptional decision-making and critical-thinking skills can pursue bachelor's and master's degrees with a specialisation in data science. The choice depends on students' interests.