AI Career Path: A Guide to Essential Skills, Certifications, & Job Prospects in 2025

Latest Update: The AI market is projected to reach INR 20,22,200 crore by 2025, with a CAGR of 26.60% from 2025 to 2031, driving a market volume of INR 83,14,200 crore by 2031.

In 2025, AI roles such as Machine Learning Engineers and AI Researchers are offering competitive salaries, with Machine Learning Engineers earning an average of INR 10L to INR 30L annually! Key skills required include Python, machine learning, deep learning, and cloud computing. 

This blog highlights top AI job roles, required skills, certifications, and salary ranges in 2025, helping you plan your career path.

What to Expect in the AI Career Path? Concepts & Applications

AI and machine learning are creating new ways of working, solving problems, and driving innovation. Healthcare uses AI to predict disease patterns and finance uses it to improve customer experiences through personalized recommendations. 

This is the future of work, and there’s a huge opportunity for you to get involved.

So, what exactly can you expect from a career in AI and ML? Let’s break down the key concepts and applications that will shape your path.

What is AI & Machine Learning?

Artificial Intelligence (AI) refers to the creation of machines that can perform tasks that typically require human intelligence, like understanding language, recognizing patterns, and making decisions. Machine Learning (ML), a subset of AI, is the process where machines use data to learn and improve without being explicitly programmed.

Here are some core principles driving AI/ML:

• Learning: Machines enhance their performance through experience and data.
• Automation: AI/ML eliminates the need for manual intervention in tasks.
• Adaptation: Systems continue to evolve and get better over time, without human intervention.

The distinction between AI and ML is key. AI is the broader field focused on creating intelligent systems, while ML specifically revolves around how these systems learn and adapt from data.

The key subfields in AI/ML include:

• Natural Language Processing (NLP): Empowering machines to understand and generate human language, which powers chatbots, virtual assistants, and translation services.
• Computer Vision: Teaching machines to interpret visual data, used in facial recognition, autonomous vehicles, and medical imaging.
• Robotics: Combining AI with physical systems to carry out tasks like assembly, delivery, or surgery.

These core concepts form the backbone of machine learning careers, and understanding them is critical as you chart your AI career path.

Also Read: Computer Vision - Its Successful Application in Healthcare Security Transportation Retail

Job Opportunities for Machine Learning in 2025

As AI and ML become more integrated into business processes, the demand for skilled professionals in the field is growing exponentially. The AI job market has already witnessed a significant growth of 42% over two years, with overall hiring up by 31%.

Here are some industries that are adopting AI the fastest:

1. Healthcare: Revolutionizing Patient Care

AI is enhancing diagnostics, personalizing treatments, and speeding up drug discovery. AI healthcare professionals are crucial for developing predictive models that help doctors make better decisions. Medical AI Engineers design systems that analyze medical images or predict disease, playing a direct role in improving patient outcomes.

Career Path: Starting as a Data Scientist, you could evolve into an AI Clinical Researcher or AI Program Manager, leading AI initiatives in healthcare.

2. Finance: Shaping Risk and Investment Strategies

In finance, AI drives fraud detection, risk management, and predictive analytics for investments. AI Quantitative Analysts develop algorithms to predict market behavior, while Machine Learning Engineers optimize trading strategies and reduce risk.

Career Path: You could begin as a Machine Learning Engineer or Quant Analyst, advancing to roles like Lead Data Scientist or AI Strategy Lead as you gain experience.

3. Retail: Personalizing Experiences and Optimizing Operations

AI is personalizing customer experiences and optimizing inventory in retail. AI Retail Analysts use machine learning to create recommendation systems, forecast demand, and optimize pricing.

Career Path: Starting as a Data Scientist, you could move into roles like AI Product Manager or Operations Lead, focusing on AI-driven strategies for product development and supply chain management.

4. Cybersecurity: Proactively Defending Against Threats

AI in cybersecurity identifies threats and detects vulnerabilities in real time. AI Security Engineers develop systems that use machine learning to spot unusual activity and prevent attacks.

Career Path: You could begin as a Security Data Scientist, transitioning into roles like AI Security Architect or Head of AI Security Operations, designing large-scale AI defense systems.

5. Manufacturing: Enhancing Efficiency and Predicting Maintenance

In manufacturing, AI is used for predictive maintenance and optimizing production lines. AI Engineers create models that predict equipment failures, minimizing downtime and improving efficiency.

Career Path: Starting as a Machine Learning Engineer, you could progress to roles like AI Systems Engineer or Manufacturing AI Strategist, overseeing AI-driven operations and optimizations.

Why AI & ML Are Booming in 2025: The popularity of AI is tied to its ability to solve complex challenges that traditional systems can’t. Businesses use it to increase productivity, improve customer satisfaction, and reduce costs. 

The AI talent gap is widening, and throughout 2025, millions of AI-related roles are expected to remain unfilled. This creates an immense opportunity for professionals like you to enter the field.

The demand for AI talent is driving salary growth, and this trend is expected to continue until 2030. 

Also Read: Career Opportunities in Artificial Intelligence in 2025

Career Prospects in India vs. the Globe: AI and ML are experiencing explosive growth worldwide. Globally, the AI market is expected to reach $190 billion by 2025, with demand for AI professionals skyrocketing across sectors like healthcare, finance, and retail. 

In India, the AI job market is driven by an increase in AI startups and growing adoption of AI by both established companies and emerging industries. India’s AI market is projected to grow at a CAGR of 40%. This is creating a need for skilled professionals in roles such as AI engineers, data scientists, and ML researchers.

In tech hubs like the U.S., Europe, and China, companies are aggressively hiring to keep up with AI advancements. India’s booming tech sector, particularly in cities like Bangalore, Hyderabad, and Pune, is seeing a growth in AI-related job openings. 

Whether you're eyeing opportunities in global tech giants or fast-growing Indian startups, the AI/ML job market offers abundant prospects for those with the right skills and certifications.

The real question is: Are you ready to build the expertise that will position you for success in this thriving market?

Now that you have a basic understanding of the job opportunities for machine learning, let’s explore some of the essential skills needed for building a career in AI.

Essential Skills Required for an AI Career Path in 2025

The AI job market demands a combination of advanced technical abilities and strong soft skills. Learning the key tools, languages, and problem-solving techniques specific to AI and machine learning will directly impact your career growth and opportunities. 

Let’s dive into the essential skills you need to stand out in this competitive landscape.

Technical Skills

In the AI career path, technical skills are the foundation of your success. The ability to understand and apply machine learning algorithms, work with large datasets, and deploy models effectively will be in high demand. 

Here are the areas you’ll need to focus on:

1. Programming Languages: Python is the go-to language in AI, with most job postings requiring it as a primary skill. R and Java are also popular, especially in specific niches like data analysis and large-scale applications.

2. Machine Learning & Data Structures: Understanding algorithms and data structures is a must. You’ll need to grasp how machines learn from data, which requires knowledge of decision trees, linear regression, and clustering techniques.

3. Deep Learning & Neural Networks: These are the backbone of modern AI. From image recognition to autonomous vehicles, deep learning will be a major area of focus. Neural networks are essential for tasks involving complex data like images, speech, and text.

4. Natural Language Processing (NLP): NLP is essential if you’re interested in AI applications that interact with humans, like chatbots, virtual assistants, or language translation tools.

5. Data Science & Big Data Analytics: AI thrives on data. Being able to analyze large datasets efficiently is key. You’ll need to be comfortable working with big data tools, and understanding statistical analysis will give you an edge.

6. Cloud Computing: AI models require substantial computational power. Cloud platforms like AWS, Azure, and GCP are central to running AI models at scale, making cloud knowledge essential.

7. MLOps & Model Deployment: It’s not enough to build models; you need to know how to deploy them effectively in different environments. MLOps skills ensure models can be monitored, maintained, and scaled efficiently.

Soft Skills

While technical expertise is vital, soft skills play an equally important role in shaping your career. Here are the soft skills that will help you excel:

1. Problem-solving: AI is about solving complex problems. Whether you're developing new algorithms or troubleshooting model performance, strong problem-solving skills are essential.

2. Analytical Thinking: AI requires a logical, data-driven approach. Analytical thinking will help you interpret data, make informed decisions, and find insights others might miss.

3. Communication & Teamwork: In AI, you’ll often work in teams, collaborating with data scientists, engineers, and business stakeholders. Being able to communicate complex ideas clearly and work effectively in a team will make you more valuable.

The combination of technical prowess and strong soft skills will give you a well-rounded edge in the AI job market. 

Also Read: How To Improve Your Career Skills for a Better Job and Employability [2025]

Along with these skills, you’ll need to know some AI/ML methodologies that are commonly used in these job roles.

AI/ML Methodologies: Proven Approaches and Techniques for Success

An AI career path in healthcare, finance, or any other sector requires a strong understanding of the core methodologies and techniques behind AI/ML. To succeed, you must understand each phase: starting with data collection and preparation, followed by training and evaluation, and finally deploying models that deliver real value.

Each step of this process is crucial in ensuring the model functions correctly, and skipping any part can result in subpar performance. 

Here are some key AI techniques followed during model deployment:

1. Data Handling: The Foundation of AI/ML

Before building any model, data handling is where the journey begins. The success of any AI/ML model depends entirely on the quality and relevance of the data it's trained on.

Proper data handling ensures that your model has accurate, consistent, and reliable information to learn from. This involves data collection, cleaning, and preprocessing.

• Collection: Gather data from relevant sources, whether structured (databases) or unstructured (images, text, audio). The key here is ensuring that data is both high-quality and relevant to your problem.
• Preprocessing: This stage involves cleaning the data, dealing with missing values, removing outliers, and normalizing it for further analysis. The goal is to transform raw data into a form that is ready for machine learning algorithms to work with.

Fact: Studies show that 80% of time in AI/ML projects is spent on preprocessing. This includes tasks like data wrangling, handling missing values, and ensuring consistency across datasets. Without solid data preprocessing, even the most advanced models can perform poorly.

Good data handling is the bedrock of successful AI/ML applications. Poor data quality or inaccurate preprocessing can undermine the entire model's effectiveness.

2. Model Selection and Training: Building Effective Models

Once you’ve got clean data, it’s time to build and train your models. The most critical decision here is model selection, which involves choosing the right algorithm for your problem. 

Whether you’re working on classification, regression, or clustering, you’ll need to pick a model that can best handle your dataset.

Once the model is selected, the next step is training. During this phase, the model learns from the data by adjusting its parameters. The more diverse and representative your data, the better your model will perform.

3. Evaluation and Deployment: Putting Models to Work

After training, it’s time to evaluate your model’s performance. Metrics like accuracy, precision, recall, and F1 score will tell you how well your model is performing. It’s crucial to test the model with unseen data (this is called validation) to make sure it can generalize well to specific scenarios.

Once you’ve validated the model, it’s time for deployment. This means putting the model into production where it can make predictions in real time. Here, techniques like MLOps (Machine Learning Operations) come into play, ensuring smooth deployment, monitoring, and continuous improvement.

Stat: In industries like healthcare, the introduction of deep learning models has led to up to 40% improvement in patient outcomes.

These approaches and techniques in model deployment are the building blocks for successful AI applications.

Also Read: Top 5 Machine Learning Models Explained For Beginners 

Knowing the various techniques and possessing the required skills isn’t enough. You must know how to apply them, and this is where AI/ML certifications can help you.

Most In-Demand AI/ML Certifications for 2025

Certifications in AI and machine learning (ML) are becoming increasingly relevant as businesses across industries strive to integrate advanced technologies into their operations. As the demand for AI-driven solutions grows, organizations need professionals with the expertise to design, implement, and maintain AI systems. 

These certifications serve as a formal recognition of a professional's skills and knowledge, offering several key benefits:

• Certifications demonstrate that you possess the specific skills and knowledge, making it easier for employers to identify qualified candidates.
• Many leading tech companies offer specialized AI and ML certifications that are recognized and respected globally, making them valuable assets on your resume.
• Certified professionals are often seen as more competitive candidates for job roles and promotions.
• Having the right certifications can help you qualify for high-paying roles in various sectors, such as technology, healthcare, finance, and automotive.
• Certification programs often provide opportunities to interact with professionals in the field through forums, workshops, or study groups.

Now, let’s explore the most relevant AI and ML certifications for 2025.

Top AI Certifications for 2025

To succeed in AI, obtaining specialized certifications in core technologies and platforms will set you apart. Here are some of the top AI certifications for 2025:

• Professional Certificate Program in Data Science and AI: This program offers live training from industry experts and equips you with the skills to solve real challenges. It includes hands-on projects with leading companies such as Uber, Sportskeeda, and Teach for India.
• Advanced Generative AI Certification Course: This is a 100% application-focused Generative AI certification that allows you to learn through recorded sessions and live masterclasses. For five months, you'll work on AI projects, including intelligent chatbots, image generators, and knowledge retrieval systems.
• Google TensorFlow Developer Certificate: Focuses on deep learning and TensorFlow, a leading platform in AI development. Perfect for those interested in implementing machine learning models.
• AWS Certified Machine Learning – Specialty: For professionals looking to design, implement, and maintain machine learning solutions using AWS. Recognized for cloud computing integration with machine learning.
• Microsoft Certified: Azure AI Engineer Associate: Ideal for those working with Microsoft's cloud platform, Azure, to build and manage AI solutions at scale.

These certifications are designed for those who want to validate their skills in AI technologies like TensorFlow, AWS, and Azure. Whether you’re looking to specialize or broaden your skills, these certifications offer solid pathways into high-demand roles.

Fact: Certified AI professionals earn 20-30% more than non-certified professionals, reflecting the value of validated expertise in the job market.

Top ML Certifications for 2025

Machine learning is a specialized field within AI, and there are certifications that focus purely on ML applications. Here are the top ML certifications for 2025:

• Post Graduate Certificate in Machine Learning and Deep Learning (Executive): This course is powered by upGrad with over 2 million learners and 300+ hiring partners. It combines 240+ hours of rigorous learning and 5+ industry-relevant projects, making it the most comprehensive program of its kind.
• IBM AI Engineering Professional Certificate: Covers machine learning techniques, deep learning, and practical applications. A great choice for those aiming to build robust machine learning systems.
• Stanford University Machine Learning (Andrew Ng): One of the most popular ML certifications, this course offers deep insights into machine learning algorithms and their applications.

These certifications are perfect for professionals who want to develop expertise in core machine learning methods, such as neural networks and natural language processing (NLP). 

Also Read: Top 10 Best Machine Learning Courses: 2025 Edition

Before starting your learning journey, knowing which job roles pay the most may help you in making your decision.

AI & Machine Learning Job Roles and Salary Trends

AI and Machine Learning (ML) roles are in high demand, with salaries reflecting the specialized skills required. For example, AI engineers and machine learning engineers often earn higher salaries due to their expertise in building complex systems that drive automation and efficiency. 

Data scientists also see competitive pay, especially when working with large datasets and advanced analytics. Roles like NLP engineers are increasingly in demand as businesses look to improve customer interactions with voice recognition and chatbots, leading to higher salary offerings in this area. 

Salaries can vary significantly based on experience, with senior positions and those requiring deep expertise commanding the highest pay. It can also vary by location.

Here’s a comparison of average salaries in key countries:

Source: Builtin, Glassdoor

Fact: Compared to traditional IT roles, AI specialists can command a salary premium of up to 40-80%.

Entry-Level Jobs

At the entry level, AI and ML roles offer competitive salaries, but you will need to gain experience through projects, internships, or freelance work to move up.

Source: Glassdoor

Mid-Level Jobs

At the mid-level, you’ve gained some experience and likely have a good grasp of your area of expertise, allowing you to handle more complex tasks.

Source: Glassdoor

Senior-Level Jobs

At the senior level, AI professionals play a strategic role in shaping and implementing AI strategies for their organizations. These roles typically involve leadership responsibilities.

Source: Glassdoor

Freelancing & Entrepreneurship in AI

AI professionals are also finding success as freelancers and entrepreneurs. As businesses look to adopt AI solutions but lack in-house expertise, freelance AI consultants are in high demand. In addition, AI-focused startups are thriving, especially in areas like healthcare AI, autonomous systems, and AI-driven software. 

The flexibility and high pay potential in freelancing can be attractive, but it requires a solid reputation and a network of clients.

Source: Glassdoor

The AI and ML job market is high-paying, with salaries that are significantly higher than those for traditional software engineering roles. The demand for AI talent is only expected to grow, making it an exciting time to be in the field.

Also Read: How to Become an Artificial Intelligence Engineer? Salary, Skills & Steps

Next, let's go over some of the leading companies that are hiring AI and ML engineers.

Top Companies Hiring AI & ML Professionals in 2025

The demand for AI and Machine Learning (ML) professionals is growing, as companies race to integrate these technologies. This has created a talent shortage, with the need for AI/ML specialists expected to grow by 40% in the coming years.

Here are some of the popular organizations for AI and ML roles:

• Google: Recognized as a major employer in the AI sector, Google consistently expands its AI team to enhance products and services. AI engineers can make anywhere between INR 10.3L to INR 5.3Cr at Google.
• Microsoft: With substantial investments in AI, including the launch of new data centers in Malaysia, Microsoft continues to recruit AI specialists to support global initiatives. ML engineers get paid from INR 8L to INR 24L annually on average.
• Amazon: Amazon's commitment to AI is evident through its development of advanced technologies like generative AI tools for advertising, creating a demand for skilled AI professionals. ML engineers at Amazon can earn anywhere between INR 4L to INR 18L annually.

Emerging AI Startups to Watch in 2025

The startup ecosystem is vibrant with AI-focused companies driving innovation across various sectors. Notable startups include:

• Reclaim.ai: An AI-driven smart calendar application optimizing scheduling for professionals and teams.
• People.ai: A revenue intelligence platform utilizing AI to enhance sales and marketing efforts. 
• Seamless.AI: An AI-powered platform providing real-time access to accurate contact information for sales professionals.
• Grammarly: An AI-driven writing assistant enhancing communication clarity and effectiveness.
• Tessian: An AI company focusing on email security, protecting organizations from advanced threats.

Global AI Job Hotspots

Several countries have emerged as prominent hubs for AI talent, offering robust job opportunities and fostering innovation. Apart from India, you can also find great career options in artificial intelligence across the globe.

Here are some notable hotspots:

• United States: Home to numerous tech giants and startups, the U.S. leads in AI job creation, particularly in regions like Silicon Valley.
• Canada: With favorable immigration policies and a growing tech sector, Canada attracts AI professionals, especially in cities like Toronto and Vancouver.
• Germany: Recognized for its strong engineering base, Germany offers AI opportunities, particularly in automotive and manufacturing sectors.
• China: Significant investments in AI research and development position China as a key player, with cities like Beijing and Shenzhen being focal points.
• Singapore: Strategic initiatives to become an AI hub make Singapore attractive to professionals in the field.

Staying informed about these trends can guide professionals in making strategic career decisions in the evolving AI landscape.

Also Read: Top Companies Hiring Data Scientists in India

Now that you’re fairly familiar with the AI/ML job market, let’s explore how you can get started on your learning journey.

AI & Machine Learning Roadmap (Step-by-Step Guide)

If you're looking to break into AI and Machine Learning (ML), there's a clear path to follow. By taking the right steps, you can quickly build the skills needed to land a rewarding job in this growing field. 

Here's a step-by-step guide to help your learning journey.

Step 1: Learn Programming (Python, R, Java)

The first step is learning a programming language. Python is the most popular language for AI and ML due to its simplicity and vast library support. R is great for data analysis, while Java is widely used for large-scale AI systems. 

You don’t need to be an expert in all three, but being proficient in Python will give you a solid foundation to work from.

Step 2: Understand Math & Statistics (Linear Algebra, Probability, Calculus)

AI and ML are built on solid mathematical principles. You’ll need to understand linear algebra (for dealing with matrices), probability (for decision-making models), and calculus (for optimization). 

These subjects are vital for building algorithms and understanding how AI systems learn and make predictions.

Step 3: Get Hands-on with Machine Learning Libraries (TensorFlow, PyTorch, Scikit-learn)

Once you're comfortable with programming and math, dive into ML libraries. TensorFlow and PyTorch are the two leading frameworks for deep learning, while Scikit-learn is excellent for classical machine learning. Practicing with these tools will help you get a feel for how to build, train, and test models.

You can also improve your knowledge of Python Libraries with upGrad’s Learn Python Libraries, which can give you a significant edge in your AI career path.

Step 4: Work on AI Projects

Theory is important, but practice is essential. Start building your own AI projects. This could include things like creating a recommendation system, image classifier, or chatbots. Actual projects will deepen your understanding and make your skills more attractive to employers.

Step 5: Earn AI Certifications

Certifications show you have the skills and knowledge to work in AI. Completing programs like the Google TensorFlow Developer Certificate or the IBM AI Engineering Professional Certificate can enhance your credibility and make you stand out in the job market.

Step 6: Build a Portfolio & Apply for AI Jobs

Finally, create an online portfolio to showcase your projects. Platforms like GitHub are great for sharing your code. A strong portfolio combined with your certifications will make applying for jobs much easier. When you're ready, start applying for positions like AI engineer, data scientist, or ML researcher.

Stay focused, build your skills step by step, and don’t forget to keep learning. AI is evolving fast, and continuous improvement is key.

Also Read: Machine Learning Career Path: 12 In-Demand Roles, Skills, and Salary Insights

However, while learning, you might face some challenges. You can overcome them by following some best practices.

AI & ML Career Challenges & How to Overcome Them

The AI and ML field offers exciting career opportunities, but it also comes with its own set of challenges. As the industry grows, professionals face obstacles like a steep learning curve, rapidly evolving technologies, and high competition for jobs. 

However, with the right approach, you can overcome these challenges and thrive in your AI career:

A Closer Look at the Ethical Concerns & AI Regulations

As AI continues to influence more aspects of life, ethical concerns and regulatory issues become more important. 

Here's a quick breakdown of the key ethical consideration in AI:

• AI systems can inherit biases from the data they are trained on, which can lead to unfair outcomes, especially in sensitive areas like hiring or law enforcement.
• With AI relying heavily on data, ensuring privacy is a big challenge. Regulatory frameworks like GDPR are being put in place to protect users' personal information.
• Automation through AI has the potential to replace certain jobs, raising concerns about the future of work and economic inequality.
• AI systems are often seen as "black boxes" where their decision-making process isn't always clear. Ensuring transparency in AI models is crucial to building trust.
• Governments around the world are increasingly introducing regulations to address the ethical use of AI. Staying compliant with these laws is key to avoiding legal issues.

AI and ML offer a rewarding career path, but the challenges are real. By staying informed, building your skills, and actively engaging with the latest developments, you can overcome these obstacles and advance in your AI career.

Also Read: AI Ethics: Ensuring Responsible Innovation for a Better Tomorrow

Next, look at some future trends in AI and ML that you will want to be prepared for so you can excel in your AI career path.

Future Trends in AI & Machine Learning Careers

As automation grows, we’re not just seeing job displacement, but the emergence of hybrid roles that blend technical expertise with ethical responsibility. This opens up opportunities for careers that combine innovation, tech, and regulation.

The demand for AI ethics and governance professionals is also growing, as companies seek to ensure AI systems are fair, transparent, and aligned with societal values. Ensuring this balance between technology and accountability will be key to staying ahead in the AI field. 

Here are some key trends shaping AI and ML careers:

• AI-powered automation is transforming industries, replacing repetitive tasks while creating new roles that require specialized skills.
• As the Metaverse and Web3 gain momentum, AI is becoming essential for creating immersive experiences and enabling decentralized systems.
• As AI becomes more integrated into society, there's a growing need for professionals who can ensure AI systems are ethical and compliant with regulations.

AI Replacing Traditional Programming Jobs?

AI is increasingly capable of automating tasks traditionally done by human programmers. While AI will automate some aspects of programming, it’s more likely to complement developers rather than replace them entirely. 

Here’s a look at how AI may affect programming jobs:

• AI for Code Generation: Tools like GitHub Copilot are already helping developers by suggesting code and completing repetitive tasks, increasing efficiency.
• Shift to Higher-Level Tasks: AI may automate basic coding tasks, but developers will still be needed for higher-level problem-solving, architecture design, and ensuring AI systems are aligned with business needs.
• New Roles: AI will also create new roles for AI trainers, AI ethics specialists, and ML model evaluators, emphasizing the human touch in the development and regulation of AI systems.

Also Read: Top AI Companies Revolutionizing Industries with Cutting-Edge Technology

By 2025, it’s expected that a significant percentage of businesses will use AI-powered automation to streamline their operations. Focus on continuous learning and adapt to the evolving AI landscape to stay ahead.

Conclusion

AI and Machine Learning are transforming industries, with 75% of enterprises expected to adopt AI for automation by 2030, generating INR 1,066 trillion in economic value. As these technologies evolve, AI professionals will be key in driving change, not just through technical expertise, but by integrating ethics, governance, and societal impact into AI solutions.

Now is the time to position yourself in this fast-growing field. Start by gaining hands-on experience with AI projects, equipping relevant certifications, and exploring specialized courses to build your skills. Consider joining AI communities and staying updated on industry trends. 

Not sure where to start? Connect with upGrad’s career counseling for personalized advice on mapping out your AI career path. Or, head to your closest upGrad center to begin hands-on training and gain actual experience in AI.

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