How to Make a Career Switch Into AI 

There is a huge demand for AI engineers. US foreign-labor hires analysis shows base salaries for AI engineers is around $152K per year. It’s no secret the demand for AI talent is outpacing supply, and now is a great time for a career switch into AI.

Analytics Insights predicted new AI jobs will increase at a compound annual growth rate of 31.6% and reach about 20 million by 2023. However, there are many vacancies due to the skill gap, leading to a trend of late adopters learning AI skills and joining the industry. Even seasoned IT professionals can feel trapped in their job. Pursuing a new career in AI is worth considering. 

Why pursue an AI career now? 

  • Secure and high-paying jobs
  • Opportunity to solve real-time problems 
  • Continuous learning to eradicate monotonous tasks

Pathway to becoming an AI engineer 

– Master the Fundamentals of AI

Understanding AI basics is the first step to a successful career in the field. Most people associate artificial intelligence with robots and voice assistants, however, there is more to it. Learning the basics helps you discern between common knowledge AI and what it really is, informing your decision to pursue a career around it. 

– Solidify your Technical Skills

We can break down the technical skills required to be a successful AI engineer into four categories. Having some degree of expertise in each of these is essential when making a career switch: 

i. Programming Skills

You must be well-versed in programming languages such as Python, Javascript, R, and C++ to build and implement AI models. 

ii. Linear Algebra, Probability, and Statistics

To implement and understand different AI models, such as Naive Bayes and Hidden Markov model, you need a comprehensive knowledge of these.

iii. Spark and Big Data technologies 

AI engineers work with large amounts of data. Sometimes production-level data reach terabytes or even petabytes. You must know about big data technologies Apache spark, Hadoop, MongoDB, and Cassandra.  

iv. Frameworks and Algorithms 

Understanding AI/ML algorithms like SVM & Logistic regression helps you implement AI models. Frameworks like PyTorch, TensorFlow, Theano, and Caffe help you work with these algorithms and data. 

– Seek AI Courses 

After you have solidified your technical AI skills, it is best to proceed by taking additional AI courses. These courses could let you get familiar with how AI is used for solving real-world problems. like using ML for traffic prediction or speech recognition. Free and paid courses are available in AI subfields such as machine learning, deep learning, computer vision, and natural language processing. 

– Get Certified and Involved in AI-based Projects

After completing some courses, you must gain practical knowledge and experience. Start with some internship opportunities or fellowship programs. In the Fusemachines AI Fellowship, AI enthusiasts are given the platform and opportunity to pursue their AI career with professional guidance. You can search and apply for the best companies and projects to add relevant experiences and make this career switch work.  

– Emphasize your Soft Skills and Keep Learning

Soft skills are essential to complement your technical expertise. Communication, emotional intelligence, critical thinking, adaptability, and positive work ethics can help you stand out over the competition and take the best AI jobs available out there. 

Common mistakes while making AI career switch

  • Your decision to switch is practical but not passion-driven.
  • Struggling to start whole-heartedly in a new field. 
  • Hesitant to seek out help or join relevant communities.  
  • Not being able to work in a team. 
  • Not thinking you deserve the job, project, or promotion. Failure is always around the corner if you lack self-belief.  
  • AI might need you to be critical and think outside the box.
  • Limitless opportunities could be overwhelming and lead to feeling stuck 
  • Quitting too soon. You won’t be an AI professional overnight. It is a continuous learning and development process. 

Bottom line: 

Having clarity is important before choosing any career in AI engineering. Know where to start, what languages and technologies to learn, which communities and courses to seek out, and what sub-field of AI amongst many to aim for beforehand. If you are willing to pursue a career in AI, try understanding the industry, steps, and everything involved first. Once you’ve made this career switch, platforms like squadery will then help you get your dream AI job and projects in America. 

Learn to build a fantastic AI engineer resume. Sign up for our webinar. 

Looking for remote AI engineering jobs in top US tech companies? Join Squadery.