A new year often sparks a fresh start; for many, that means exploring new career opportunities. The idea of a career pivot feels exciting—almost irresistible—with the promise of higher earnings and fresh challenges. Among the most enticing paths? Artificial intelligence roles. These positions are quickly becoming goldmines as companies realize AI’s immense value in boosting productivity and precision. Over the past year, job postings for generative AI roles have skyrocketed, increasing tenfold.

AI presents some of the most lucrative and future-proof opportunities for professionals looking to pivot their careers or enhance their skill sets. As industries increasingly integrate intelligent systems into nearly every aspect of their operations, proficiency in AI is no longer optional—it’s essential.

From developing machine learning models to leveraging AI tools for streamlining workflows, mastering these skills has become critical to staying competitive in the evolving job market.

3 AI Positions That Pay Well

Machine Learning Engineer

Machine Learning Engineers are the backbone of AI development. They design and implement algorithms that enable systems to learn from data, making them crucial to applications like recommendation engines, predictive analytics and fraud detection. The earning potential is upwards of $200,000 a year.

Their primary responsibilities include building and optimizing machine learning models to solve complex problems and enhance system performance. They are also tasked with deploying scalable AI systems in production environments, ensuring reliability and efficiency. Collaboration is a key aspect of their work, as they often partner with data scientists and software engineers to design and implement innovative solutions.

Skills to Develop:

• Programming languages—Master Python, R and Java
• Frameworks and libraries—Learn TensorFlow, PyTorch and Scikit-learn for building machine learning models.
• Mathematical foundation—Develop a strong understanding of linear algebra, calculus and statistics.
• Cloud platforms—Familiarize yourself with AWS, Google Cloud or Azure to deploy machine learning solutions.

How to Transition:

Start with online certifications like Google’s Professional Machine Learning Engineer or Coursera’s Machine Learning Specialization by Andrew Ng. Apply your skills to real-world projects through platforms like Kaggle or GitHub to showcase your portfolio. For beginners, transitioning from software engineering or data analysis offers a smoother path into this role.

ForbesWhy Consensus Leadership Is Killing InnovationBy Cheryl Robinson

Natural Language Processing Specialist

NLP specialists are at the forefront of bridging human language and machine understanding, enabling machines to understand, process and generate human language. They are behind the development of chatbots, sentiment analysis tools and advanced language models like OpenAI’s GPT. The earning potential is upwards of $180,000 a year.

They train advanced models for tasks such as speech recognition, language translation and text summarization, ensuring accuracy and efficiency. Additionally, they tackle complex linguistic challenges using AI, creating solutions that push the boundaries of what technology can achieve in understanding human language.

Skills to Develop:

• NLP libraries—Gain expertise in spaCy, NLTK and Hugging Face.
• Deep learning models—Learn to work with transformer models like BERT, GPT and T5.
• Linguistics and semantics—Develop a foundational understanding of grammar, syntax and semantics.
• Data Preprocessing—Master techniques to clean and structure text data.

How to Transition:

Begin with resources like the NLP Specialization by Deeplearning.ai or fast.ai’s NLP course. Transitioning from a linguistics, computer science or data science background can be beneficial. Freelancers can gain hands-on experience by developing AI-driven chatbots or performing text analysis for small businesses.

AI/ML Product Manager

AI/ML product managers serve as the bridge between business and technology. They play a pivotal role in successfully developing and implementing AI solutions. Additionally, they are responsible for managing the entire lifecycle of AI products, from initial ideation to final deployment, ensuring alignment with business goals and market demands. The earning potential is upwards of $180,000 a year.

A key aspect of their role is translating complex business needs into clear, actionable technical requirements. They also lead cross-functional teams of engineers, data scientists, and stakeholders, fostering collaboration and driving projects to completion.

Skills to Develop:

• AI fundamentals—Acquire a working knowledge of machine learning concepts and tools.
• Project management—Learn agile methodologies and tools like Jira or Trello.
• Business acumen—Develop skills in market analysis, competitive research and ROI assessment.
• Communication—Hone your ability to convey technical ideas to non-technical stakeholders.

How to Transition:

Professionals transitioning from project management, product development or even marketing can pivot to this role by completing certifications like Stanford’s AI Product Manager Certificate or LinkedIn Learning’s Agile AI course. You can also build your experience by managing small AI-focused projects in your current or freelance role.

As AI reshapes the professional landscape, it presents unparalleled opportunities for those ready to embrace its potential.