John McCarthy, the father of AI, coined the term Artificial Intelligence as “the science and engineering of making intelligent machines.” AI technology has evolved over the years and changed the way we live. As Forbes announced 2020 as the year of AI, this is the right time to carve out a career transition path in Artificial Intelligence. We live in an era of information with companies having tons of both structured and unstructured data. Needless to say, we are in high demand for AI Engineers who can use raw data to solve complex problems by developing AI-led applications. The average salary of an AI Engineer ranges between ₹3,64,000 — ₹15,28,000 ($ 5–21 thousand) according to Glassdoor. This blog explains who is an AI Engineer, what do they do? and How to build a career path for a succesful career transition in the Artificial Intelligence field. We asked our AI expert and a mentor at Springboard to understand what are the prerequisites to become an AI Engineer and how to acquire them from her own learning curve.

Career Transition to Become an AI Engineer

Although Artificial Intelligence (AI) has been around for many decades, building a career path in such a dynamic field requires a good focus and learning approach, along with an expert’s point of view. Prior experience in Statistics and programming is a plus to learn AI. In conversation with Springboard, Pavithra, a Staff Data Scientist at Swiggy, shared her journey of breaking into Data Science and AI from being a management Consultant. Describing her initial phase of transitioning into AI, she suggests you begin by justifying your career path move-

#1. Career Transition: What career is right for me?

AI approaches have many significant effects on our lives today and demand a huge talent pool of humans to incorporate them. This means we will have to enhance our existing skills and learn new robotics and AI-based software tools. To enter the AI domain, a bachelor’s or master’s degree in Statistics and Programming definitely adds on. As Pavithra stated- “Her interest and passion for data, and the ability to develop useful insights“ were the key factors that helped her decide the career move into AI.

She landed her first job as a Software Engineer at Microsoft. Soon she developed an interest in innovation consulting that involved business management. Her strong background in Software Engineering and business insights led her to solve complex business problems. The idea of designing a product to use AI in videos struck her mind. In the process, she effortlessly started working in Computer Vision and NLP

“To build a career in AI, you must be passionate about Data Science, Mathematical logic, CS and Programming, and Machine Learning. You must be willing to constantly learn and grow.”, she said.

I am sure this will certainly help you to dive into AI, just like Pavithra did.
Moving on, although AI is a diverse technology and has a variety of job roles under its spectrum. Let’s understand the most desired job role of an AI engineer.

#2. What does an AI Engineer do – The Job Description

“AI engineers blend in with data engineering, data science and software development skills to solve real-world problems by creating AI-infused applications. These apps are used to make machines learn on themselves and make decisions by feeding Machine Learning (ML) algorithms. AI Engineers mainly specialize in creating and deploying ML algorithms and maintaining the AI infrastructure. AI Engineer and ML Engineer are synonyms terms in many industries. Here is a sample job posting for AI Engineer on Indeed : 

Career-Path
Source:  Indeed

This role requires AI, ML and Statistical concepts along with big data tools. Yet another post with an emphasis on AI skills:

career-path
Source:  Indeed

The highlighted key skills in the above post include – 

  • Development and Maintenance of AI Infrastructure and data sets.
  • Data preprocessing and modelling pipelines.
  • Deep Learning, Computer Vision, ML and NLP. 
  • Python and its library knowledge is mandatory.

Springboard’s 1:1 career-oriented AI/ML courses are the best way to start learning these skills and be industry ready with hands-on projects. Now that we know what an AI Engineer role demands for, let’s group the prerequisites: 

#3. Career Transition: Prerequisites for becoming an AI Engineer

According to Pavithra, AI is not just about coding! It’s your vision and passion and the ability to cope up with the concepts that give you leverage.” Each one of us has a unique way of adapting things. There are no hard and fast rules in learning the prerequisites, and you may proceed in any logical manner. Although she insists on beginning with formal education and fundamental skills.

Basic education

A formal Bachelor’s degree in any of the majors such as –

  • Computer Science
  • Mathematics
  • Information Technology
  • Statistics
  • Finance
  • Economics

Is needed to get your initial concepts right. Some specialized roles may require a master’s or a PhD for deep conceptual understanding of the subject.

Skills and Tools

Artificial Intelligence (AI)

As an AI Engineer you must be able to incorporate human-like intelligence into machines to build futuristic AI-driven apps. Understand the following concepts: Machine Learning (ML), Natural Language Processing (NLP), language synthesis, computer vision, robotics, sensor analysis, optimization & simulation etc.

Machine Learning (ML)

ML is a subset of AIs and ML algorithms that train machines to learn and draw insights. Deep Learning, Decision trees, k-means clustering, random forest and Bayesian statistics, Graphical models, Regression etc.

Deep Learning (DL)

DL is a subset of machine learning that involves processing data and creating patterns in decision making. Key skills: Neural Networks, DL libraries – PyTorch, TensorFlow, Keras, MXNet, SciKit-Learn

Data management (distributed systems)

To perform ETL (Extract, Transform and Load) tasks on Terabytes of data. Big Data, Hadoop, Spark, Hive, Kafka, HDFS, Cassandra and HBase. SQL and NoSQL, Microsoft SQL, or PostgreSQL

Data Science & Analytical

DS concepts like Data structures, Statistics, Visualization, Modeling, Regression Analysis, Classification and Clustering etc.

Mathematics and Statistics 

To design and build AI/ML models you must excel in Logics, Linear Algebra, Calculus, Probability, Algorithms, Bayesian Statistics, and Numerical Methods.

CI/CD & SDLC knowledge

To facilitate data pipelines for AI/ML models you need to know – DevOps, Agile and Versioning using Git

CS & Programming

Programming experience using Object oriented and functional paradigms. R, Python (Libraries – Pandas, Scikit, Scipy, Numpy etc), Java and/or Scala

Cloud

Cloud technology to provision big data cloud infrastructure on AWS, Google Cloud, or Microsoft Azure.

Deployment and Orchestration

Containerization technology to deploy and manage cloud infrastructure and services. Using Docker, and/or Kubernetes.

Domain knowledge

To better understand and meet business and customer needs, that yields economical and productive results. Here is another interesting job posting on Indeed that precisely demands the above skills:

Source: Skills for an AI Engineer role published on Indeed

Let’s see how to acquire these skills.

#4. Career advice on how to acquire AI skills

“Getting into the AI stream needs a lot of hard work, dedication and perseverance. Pick up challenges that will lead you to struggle, learn and gain confidence,” Pavithra said. 

Career advice#1: Design a learning curve

She believes “There are numerous ways to learn like Books, videos, articles/blogs and courses. Keep yourself up to date with the trends in AI via job markets and business magazines. Take notes of your progress and stick to your plan.” Choose the method that suits you best and is effective.

Career advice#2: Network your way in

It is best to sail in the same boat as other AI aspirants. Pavithra recommended getting along with someone who has been through this journey before to stay focused on your goal. Get a support structure to help you along the way. You may attend seminars, data science and AI conferences, meetups and job fairs. Staying connected via LinkedIn and other Data Science and AI communities is also a great way to stay updated about the latest AI trends.

Career advice#3: Hands-on project experience

“Whether you are new to AI or come up with prior experience, what matters is how well you understand the concepts,” she stated. Projects are a way to prove your knowledge and get hands-on experience of working in the real-world scenario. She adds, “Do not get stuck in theory, always practice what you learnt.” Popular sites like Kaggle and KDNuggets provide real-time datasets. You can learn to develop AI models and build ML algorithms. These also provide competitions to test your skills and help you stand out.

Career advice#4: Find the Right Mentor

1:1 mentoring with personalized feedback from a real-time AI Engineer gives clarity on your career path. The real-world application experience and the footsteps taken by your mentor adds value to your learning. Timely feedback and guidance from your mentor further motivates you and keeps you on track.

#5. Career Transition: Prepping-up – Resume, jobs and the interview

Build a resume

Design your resume in an easy-to-read professional format. Make sure to mention your skills and projects accomplished. Provide a link to your portfolio and project work.

Search and apply for jobs 

The majority of jobs are listed on leading job websites like Glassdoor, LinkedIn, Naukri, Indeed and Monster. If you want to target specific job roles in certain industries, search the company’s career page. Industries also pick the best talents from project sites and community websites. 

Preparing for the Interview

Pavithra looks for “problem-solving and conceptual skills in the candidate.” You are expected to put theoretical concepts into real-world applications in order to stand out. Check out how most of the AI/ML interview questions are framed.

#6. Career Transition: Jumpstart your Career in AI

“AI is not just about tools, but how you approach a problem with applied concepts. What insights do you draw and what’s your thought process along the way?”, she concluded. Merely reading the AI concepts may sound less promising as compared to taking mentor-led job-guarantee courses at Springboard. You get personalized 1:1 mentoring and a project-driven approach in your curriculum. 

Not just this, your coach also helps you to learn the concepts right, design your portfolio, apply for the job and crack the interviews to land you an AI Engineer role.