AI Career Starting

How to Get a Job in AI? How to Become an ML Engineer or Data Scientist?

Lately, I’ve been getting asked about this more and more. Anyone looking to start a career in AI can generally be split into three categories:

• Those with development experience but not in AI
• Those without any development experience, coming from different fields
• Students and recent graduates from technical universities

It’s no surprise, really. With the advent of the latest generative models like ChatGPT and Mid-journey, interest in AI has spiked across all walks of life. Working in AI seems almost magical, and what’s more, it pays well.

Who wouldn’t want to become a well-paid wizard?

Regardless of which category you fall into, starting an AI career is achievable with the right strategy.

In this post, I’ll talk about how to develop the right mindset for learning AI, choose a field that resonates with you, land a great job, and keep pushing through the inevitable challenges that come your way.

Starting a Career: Finding Your Personal “Why”

Technical professionals often skip over chapters about mindset, mental health, or soft skills development. It feels like our technical knowledge is the only thing worthy of attention and all that’s needed to build a successful career.

This is true to a large extent, but sometimes, technical expertise alone isn’t enough. Learning a new profession is a marathon; during this marathon, you’re bound to experience ups and downs.

So, let’s first talk about why you’re interested in AI specifically, what draws you to it, and whether it’s genuinely something you need.

Identify Your Passion in AI

The field of Artificial Intelligence offers a plethora of opportunities today.

1. Professions

You can choose from various professions, from AI Researcher to AI Product Manager. Each requires developing different skills and knowledge bases, so it’s better to decide on a profession before choosing a specialization (while mastering the fundamentals, you can explore and select).

2. Application Area or Project

Few work with AI in a vacuum, for the sake of AI itself. Most companies engage in applied AI or Data Science, and what they’re working on is important for many workers.

Depending on what you find more meaningful, you can choose an area from fashion to airplane manufacturing. Remember, you may suffer at work and eventually burn out without feeling like you’re doing something significant.

3. Type of Company

Companies themselves vary from startups to large corporations, each with its own philosophy, work organization approaches, and preferences for different types of people.

In a startup, you’ll have a lot of creative freedom but will likely work a lot and often do tasks outside your job description. In contrast, a corporation will have many rules, but you’ll always be sure of tomorrow.

What Traits Do You Need for Career in AI?

What qualities do you need to be a good AI developer? The answer may depend on the specialty you choose, but in general, for any creative-technical job, it’s important to have:

• Curiosity and a love for learning
• Analytical and logical thinking
• Resilience and persistence
• Attention to detail
• Creativity and innovative thinking
• Flexibility
• Ability to work in a team

If you can say these traits apply to you to some extent, you’re likely in the right place. But be honest with yourself. If you know something isn’t typical for you, you might need to cultivate these traits further.

Seek Mentorship or Community

Interest doesn’t arise in a vacuum. Surround yourself with an information bubble dedicated to AI.

You create and maintain an interest in the area and its development by keeping up with what’s happening in the field, interacting with colleagues and like-minded individuals on professional topics, and even laughing at memes.

What can help?

Subscribe to blogs and podcasts in the apps you use most, especially during leisure time. This could be YouTube, Spotify, Medium, Quora, or Reddit. You don’t need to learn and work instead of resting; the content you fill your life with should relate to work but not be a second shift. You’re here to learn something new and fun from your professional sphere in a light format.

• The AI Podcast by NVIDIA
  It offers an easy-going conversational format that brings experts in to discuss AI and its fascinating applications in gaming, healthcare, and more, making complex subjects approachable.
  
  
• Not So Standard Deviations
  Hosts Roger Peng and Hilary Parker make statistics and data science enjoyable through their banter. They focus on the data science lifestyle and include AI topics.
  
  
• Import AI by Jack Clark
  Offers news and analysis on the latest in AI research and policy.

Find like-minded individuals. Do you have friends with whom you can discuss professional stuff informally, laugh about something, and support each other? Great if you do. If not, join communities.

It’s always better to meet people in person, but if that’s not an option right now, use internet communities like Reddit (r/MachineLearning, r/Artificial, r/deeplearning) for discussions, resource sharing, and AI-related questions.

Find a mentor. Having an experienced person nearby who knows much more than you is very beneficial. Usually, when you start a job, this isn’t a problem: companies have experts at various levels, and the most experienced often mentor junior employees in some way.

Before getting a job, this could be a teacher or a more experienced friend who can answer your questions and adjust your plans. But don’t expect a mentor to have all the answers. While this is a small but valuable help that can save you time, you’ll always have to do most of the work yourself.

Be Ready for Change

AI is a super dynamic field. Something new always pops up, and some stuff becomes obsolete and tossed aside.

Even keeping up with the latest trends is no small feat.

Be prepared for change. Learning a new profession takes time, and something significant may have changed by the time you reach your goal. But don’t worry. With the foundation and experience you’ve acquired, you’ll be able to quickly adapt to new trends and remain a sought-after professional.

Moreover, you’ll likely change your end goal along the way. Before starting actual work, many things seem different from what they really are. During your learning process, you may discover new paths and opportunities.

Tech Foundations for AI Job

Now, onto the most important thing: What to learn in 2024 to be a sought-after specialist?

In short, generative models and their adaptation to a company or client’s specific needs are at the peak of popularity today. LLMs (large language models), which I will discuss in detail in one of the following posts, are especially popular.

However, don’t get fixated on generative models and what’s trending. Trends change very quickly, and you still need to spend some time learning. Who knows what will be relevant in six months or a year?

But don’t worry! If you’re an excellent ML specialist with a solid foundation, you’ll always be able to find a job, even in areas that may not seem popular.

Be aware of what your know

Determine what you already know. And identify your strengths and weaknesses.

Some may already know how to program in Python, while others are well-versed in mathematics. Some come from management, others have no experience but are enthusiastic. Figure out what you already have and choose your AI specialization based on that.

Focus on your strengths.

Regardless, know what you can build on, and don’t be scared if you don’t have much knowledge yet.

Create a Studying Roadmap

I’m currently preparing roadmaps for mastering various disciplines necessary in AI. Stay tuned so you don’t miss out.

A roadmap is like a scheme you should ideally follow. It doesn’t always work out, and sometimes (especially if you’re dedicating time to projects), your progress through the roadmap will be somewhat chaotic. Nonetheless, it’s helpful to have one and periodically check how far you’ve progressed.

The most straightforward scheme is as follows:

1. Fundamental Skills

• Basic Software Development: OS operations, CLI, Git, anaconda
• Python: Syntax, control structures, data structures (lists, dictionaries, sets, tuples), OOP
• Math for ML/AI: Linear algebra, calculus, probability, statistics

2. Data Proficiency

• Data Analysis: Cleaning, Preprocessing, EDA techniques
• Python Libraries: NumPy, Pandas
• Database Management: Databases, Basic SQL commands

3. Machine Learning Foundations

• ML Concepts and Algorithms
• Scikit-learn for implementation

4. Deep Learning Essentials

• PyTorch
• Neural Networks: Build feedforward, CNNs, RNNs using PyTorch
• Optimization: Gradient descent, backpropagation
• Try PyTorch Lightning: Simplify PyTorch codebase

5. Advanced Models and Techniques

• Architectures (such as ResNet, YOLO, Transformers, GANs)
• Most Important Techniques: Fine-tuning, transfer learning, Dropout, Batch Normalization

6. Basic MLOps Skills

• Basic Docker 
• Basic MLFlow
• AWS SageMakes / Google Cloud

7. Specialization Areas

• Data Science: Focus on statistical modeling, data visualization
• NLP: Work with text data, transformers, seq2seq models
• Computer Vision: Advanced CNNs, object detection, image segmentation, or GANs
• MLOps: Model deployment, monitoring, CI/CD pipelines for ML

Focus on Hands-on Projects

Despite having a roadmap, dedicate more time to projects. You can find plenty of data and live examples on Kaggle. Choose the most popular datasets and notebooks that have the most community approval.

I recommend these notebooks for data analysis:

• Comprehensive data exploration with Python
• A Data Science Framework: To Achieve 99% Accuracy
• Credit Fraud || Dealing with Imbalanced Datasets
• Approaching (Almost) Any NLP Problem on Kaggle
• Interactive Intro to Dimensionality Reduction

If you wish, you can always study someone’s code on practically any topic.

You can participate in competitions after some time. I’ll discuss how to work most effectively with Kaggle later.

Stay Updated and Network

We’ve already discussed creating an information bubble around yourself. But usually, only the most hype news spreads on social networks. However, you’re interested in more than just hype as a specialist, right?

As I’ve mentioned, something new appears in AI daily, and keeping track of everything is hard. Make the delivery of AI news to your inbox automatic.

• The Algorithm by MIT Technology Review
  A weekly newsletter that offers insights and news on AI, written by the journalists of MIT Technology Review.
  
  
• Two Minute Papers
  Offers easily digestible videos that explain recent AI and computer science papers, highlighting their potential impact.
  
  
• MIT Technology Review
  Covers the latest technology news, including AI, with in-depth analysis and reports.
  
  
• AI Today Podcast
  Discusses various topics in AI and interviews industry leaders, making it accessible to a broad audience.
  
  
• AI Weekly
  A weekly roundup of the best AI articles, papers, and projects.
  

Cultivate the Right Learning Mindset

In this part, we’ll talk more about how to approach the learning process so that, darn it, you don’t give up halfway through.

Embrace Continuous Learning

Even if you’ve become an AI master, you won’t be able to stop learning continuously. So, it’s better to become a master of learning rather than just an AI master.

Remember to regularly set small learning goals, regardless of your level of mastery. Don’t scare yourself with huge goals like “I will study every day for 12 hours and learn everything in three weeks.” First, it won’t work, and second, you’ll likely quit before you finish.

Use exciting and well-structured educational materials. The internet is full of posts, courses, and collections.

Stay committed. Don’t try to cover everything simultaneously or jump between courses, concepts, etc. When you spontaneously want to switch to learning something else, jot it down and continue with what you were doing.

Establish a routine. Create a schedule you can handle, and stick to it.

Focus on Practice

Academic teachers might kill me for this advice, but I insist. One completed and functioning project in your portfolio is worth a dozen memorized formulas.

Today, AI is only sometimes pure science. Sometimes, all a developer needs is to use ready-made and semi-ready solutions effectively. Understanding the intricacies of how PyTorch functions “under the hood” is reasonable but not always necessary.

Moreover, doing projects is simply more interesting than monotonously grappling with the theoretical foundations of AI. You immediately see the result, see how good or bad it is, and become interested in improving it or doing something differently. Where interest arises, a person eventually figures out how gradient descent works.

Be Resilient in the Face of Failure

Mastering AI is a long journey. And on this path, you will face things that don’t work out and are hard to understand.

Such moments of trial often become severe strength tests, causing doubts in your abilities and loss of motivation. However, your attitude toward failures determines whether they will become stumbling blocks or valuable lessons.

Take Failures as Learning Opportunities

Every mistake, every failure in studies or job search, is not just a fall but also an opportunity to grow stronger.

Attempts that end in failure carry priceless lessons. For example, a failed interview can reveal which areas of knowledge need more attention or which soft skills need to be developed.

It’s important to analyze each such experience to understand what can be improved next time.

Don’t be Afraid of Challenges

The fear of complex tasks and potential failures often becomes the biggest obstacle to success.

However, overcoming this fear and accepting challenges makes learning more effective and exciting.

Every problem you solve and every project you work on, even if it doesn’t end in success, strengthens your professional arsenal and boosts your self-confidence.

Seek Community Support

The environment plays an invaluable role in the learning process and job search.

Exchanging experiences with like-minded people and receiving support from the community can significantly ease periods of doubt and failure. It’s important to remember that you’re not alone in your aspirations and challenges.

Participating in meetups, conferences, social media discussions, and forums allows you to share your problems, find solutions, and draw inspiration from the successes of others.

Finding Your First Job

Finding your first job can be challenging for almost everyone. I encourage you to stay calm when you start your search.

Set Realistic Expectations

Understanding that your initial role may not be your ultimate dream job is particularly relevant in the rapidly evolving field of AI. For Data Scientists and Machine Learning Engineers:

• Embrace the Learning Curve
  Early roles, even if they seem rudimentary, are good for understanding the practical applications of machine learning algorithms and data science principles.
  
  
• Value Diverse Experiences
  Projects that involve data cleaning or basic analysis are foundational. They prepare you for more complex challenges by giving you a solid understanding of the data lifecycle.
  
  
• Document Your Process and Learning
  Maintain a detailed log of your projects, including the problem, your approach, the technologies used, and the outcome. This not only serves as a personal knowledge base but also as a rich resource for showcasing your skills.
  

Create a Good Resume

Describe Technical Skills

This section is your chance to immediately capture the attention of hiring managers by demonstrating your technical toolkit. Consider organizing your skills into categories for clarity:

• Programming Languages: Highlight your proficiency in languages essential for data science and machine learning such as Python, R, and SQL. Specify your level of expertise if possible (e.g., intermediate, advanced).
• Machine Learning Tools and Libraries: Mention specific libraries and frameworks you’re skilled in, such as TensorFlow, PyTorch, Scikit-learn, Keras, and others that are relevant to your work. Highlighting experience with cloud services like AWS Sagemaker or Google Cloud ML Engine can also be a plus.
• Data Manipulation and Analysis: Showcase your ability to work with large datasets by listing experience with Pandas, NumPy, or data visualization tools like Matplotlib, Seaborn, or interactive visualization libraries such as Plotly and Dash.
• Big Data Technologies: If applicable, mention your experience with big data processing tools like Apache Spark, Hadoop, or stream-processing software such as Kafka.

Make Project Highlights

Projects are the heart of a data science resume. They provide concrete evidence of your skills and should be presented in a way that highlights your problem-solving process and results:

• Project Title and Summary: Start with a concise title and a one-sentence summary of the project goal. This helps to immediately convey the project’s relevance.
• Objective: Clearly state the problem you were solving or the hypothesis you were testing. This sets the context for your contributions.
• Your Approach/Methodology: Describe the models you built or the data analysis techniques you used. Include why you chose a particular approach over others, showcasing your decision-making process.
• Technologies Used: List the tools, programming languages, and libraries you utilized. This not only shows your technical proficiency but also gives insight into your familiarity with the current tech stack relevant to data science.
• Impact/Results: Quantify your achievements. Use specific metrics such as “increased conversion rate by X%,” “reduced processing time by Y hours,” or “improved model accuracy to Z%.” This demonstrates the tangible value you brought to the project.
• Challenges Overcome: Briefly mention any significant challenges you faced and how you overcame them. This illustrates your problem-solving skills and resilience.

Tips

• Customization: Tailor your resume for each job application. Highlight experiences and skills that match the job description closely.
• Keywords: Pay attention to keywords in job postings and incorporate them into your resume. Many companies use Applicant Tracking Systems (ATS) to screen resumes, and matching the job description’s language can help your resume get noticed.
• Professional Development: If space allows, include a section for continuous learning efforts such as online courses, certifications, workshops, or participation in hackathons and data competitions. This shows your dedication to staying updated in the field.
• Links: Consider adding a link to your LinkedIn profile, GitHub repository, or personal website where employers can see your portfolio of projects and other relevant work.

Develop a Portfolio

A portfolio for AI professionals is a showcase of your problem-solving capabilities and technical expertise.

• Host on GitHub: Include code for your projects on GitHub, ensuring your repositories are well-documented so others can easily understand your work.
• Interactive Projects: Consider including projects hosted on platforms like Heroku or Streamlit, where potential employers can interact with your models or analyses.
• Blogs and Tutorials: If you’ve written about your projects or created tutorials, include these in your portfolio to demonstrate your ability to communicate complex ideas effectively.

Seeking opportunities

Use LinkedIn and Local Job-Search Websites:

A well-curated LinkedIn profile is your digital handshake in the professional world. It should complement your resume, providing a deeper insight into your career accomplishments. Here’s how to optimize your LinkedIn profile for job hunting:

1. Profile Essentials: Ensure your LinkedIn profile includes a professional photo, a compelling headline, and a detailed summary that reflects your career objectives and skills.
2. Detail Your Projects: Highlight key projects you’ve worked on, including the technologies used and the impact of your contributions. Coders and developers should consider linking to your GitHub repository to access your code directly.
3. Publish Articles: Share insights and knowledge on topics that interest you within your field. This establishes your expertise and keeps you visible in the feed of your connections.
4. Engage in Groups: Join and participate in LinkedIn groups related to your industry. Participating in discussions can increase your visibility and help you network with like-minded professionals.
5. Job Search Websites: In addition to LinkedIn, utilize local job-search websites tailored to your region or industry. Sites like Indeed and Monster are popular globally but don’t overlook niche job boards specific to your field.

Learn About the Company:

A thorough understanding of the company you’re applying to can set you apart from other candidates. Here’s what to focus on:

1. Company Website: Start with the company’s official website, paying particular attention to the “About Us” and “News” sections to grasp its mission, values, and recent achievements.
2. Social Media and News: Follow the company on LinkedIn, Twitter, and Facebook platforms. Look for news articles about the company to understand its industry standing and recent developments.
3. Glassdoor and Similar Sites: Glassdoor offers reviews from current and former employees, giving you insight into the company culture, interview processes, and what to expect in terms of compensation.
4. Prepare Your Contribution: Based on your research, consider how your skills and experiences align with the company’s goals. Be ready to discuss specific ways you can contribute to their objectives.

Getting ready for an interview

Practice Your Technical Tasks:

When interviewing for a technical position, the ability to solve problems on the spot is crucial. A solid preparation strategy involves familiarizing yourself with the technical tasks you may encounter. Here are steps and resources to help you get started:

• Identify Common Problem Types: Research the most common technical questions related to your field. This might include algorithms, data structures, statistical questions, and machine learning model design for AI and ML positions.
• Use Online Platforms for Practice: Engage with platforms that offer coding challenges and technical problems. Some valuable resources:

• LeetCode: Offers various coding challenges that are often used in technical interviews.
• Kaggle: Provides real-world data science problems and competitions.
• HackerRank: Features various challenges across domains, including algorithms, AI, and databases.

• Implement Projects: Apply your knowledge by working on personal projects or contributing to open-source projects. GitHub is a great place to find projects that are looking for contributors.
• Join Community Discussions: Platforms like Stack Overflow and Reddit’s r/MachineLearning can provide insights into how others approach problem-solving.

Be Ready to Behavioral Questions:

Asking thoughtful questions demonstrates your enthusiasm for the role and helps you evaluate if the position and company culture align with your career aspirations. Consider preparing questions in the following areas:

Role-Specific Questions

• Can you describe a typical day for someone in this position?
• How does this role contribute to the team’s and company’s goals?

Team and Culture

• Can you tell me about the team I’ll be working with?
• How does the company support professional development and growth?

Future Prospects

• What are the team’s biggest challenges, and how can someone in this role help address them?
• What are the company’s growth plans, and how does this role contribute to that vision?

Technical Questions

• Please elaborate on the tech stack the team currently uses.
• Are there opportunities to work with emerging technologies?

Tailor your questions to each interview, reflecting your research about the company. This shows that you’ve done your homework and are genuinely interested in the position. Websites like Glassdoor can provide insights into specific companies’ interview processes and culture, offering you a better understanding of what to ask.

Improve Your English

If you’re reading this article and understand everything, you likely don’t have a problem with English. However, sometimes it’s necessary to improve your English level, especially when working in companies in English-speaking countries.

Proficiency in the English language can be a deciding factor when applying for jobs. 

Don’t rest on your laurels. Remain continually working on improving your language skills. Use courses, language practices with native speakers, and specialized resources for professionals to ensure your English is top-notch.