Building a career in artificial intelligence can feel like trying to follow a map that changes every single day. With new models and frameworks emerging constantly, how do you know which skills to focus on? For many professionals, the answer is a structured learning path that ends with a clear goal. An AI engineer certification provides exactly that. It offers a curated curriculum designed by industry leaders like IBM and Microsoft, guiding you through the essential competencies you need to succeed. It’s more than just an exam; it’s a roadmap to becoming a confident, job-ready engineer in a complex field.
Key Takeaways
- A Certification Validates Your Practical Skills: It serves as a formal credential that proves to hiring managers you can apply your knowledge, giving you a clear advantage in a competitive job market and often leading to better opportunities.
- Choose a Certification That Aligns with Your Goals: The best credential is one that fits your specific career path. Consider your current skill level, the technologies you want to master, and the industry standing of the provider to make a strategic investment in your future.
- Go Beyond the Certificate with Hands-On Practice: A certification gets you noticed, but practical experience gets you hired. Avoid the common mistake of relying on the credential alone by building a portfolio of projects that showcases your ability to apply your knowledge and solve real problems.
What is an AI Engineer Certification?
Think of an AI Engineer certification as a formal credential that proves you have the skills to do the job. It’s an official stamp of approval from a reputable organization—like Google, Microsoft, or IBM—that validates your ability to build, deploy, and manage complex AI systems. Unlike a purely academic degree, these certifications are intensely practical. They focus on the real-world tools and platforms you’ll use day-to-day, such as TensorFlow, PyTorch, and cloud services from Azure or AWS.
Getting certified shows a potential employer that you’ve not only learned the concepts but have also demonstrated your competence in a standardized, industry-recognized way. It signals that you're committed to your craft and have the discipline to master the technical skills required for a specific role. In a field that moves as quickly as artificial intelligence, a certification acts as a current, relevant benchmark of your expertise, giving hiring managers the confidence that you can step in and contribute from day one. It’s less about what you say you know and more about what you can prove you can do.
The different types of AI certifications
Not all certifications are created equal, and the right one for you really depends on where you are in your career and where you want to go. For those just starting, foundational certifications like the Microsoft Certified: Azure AI Fundamentals are perfect for building a solid base. If you’re already in the field and want to specialize, you can find programs tailored to specific domains. For instance, IBM's Generative AI Engineering certificate is ideal for anyone focusing on large language models (LLMs), while the Microsoft Azure AI Engineer Associate credential is geared toward professionals working within a cloud environment. The key is to find a certification that aligns with your goals, whether that’s mastering a specific technology or preparing for a particular job role.
Why getting certified matters for your career
In a competitive field, a certification can be the differentiator that gets your resume noticed. The AI job market is growing incredibly fast, and having a recognized credential can open doors to more job opportunities and higher salaries. Beyond just looking good on paper, the process of preparing for an exam is a huge confidence builder. It forces you to dive deep into topics like Python, natural language processing, and machine learning algorithms, helping you identify and fill any gaps in your knowledge. When you walk into an interview, you’ll have concrete proof of your skills and the confidence to talk about them articulately. It’s a powerful way to invest in your professional growth and build a career with staying power.
Top AI Engineer Certifications to Consider
With so many options available, figuring out which certification to pursue can feel overwhelming. The right choice often comes down to your career goals and the specific technologies you want to master. Do you see yourself working with a particular cloud provider like AWS or Azure? Or are you passionate about a specialized area like generative AI? The certifications below are some of the most respected credentials in the industry, offered by the tech giants that are shaping the future of AI. Each one validates a distinct set of skills that employers are actively looking for when they hire AI engineers. Let’s look at what makes each of them a strong contender for your resume.
IBM AI Engineering Professional Certificate
If you're building a foundation in AI engineering, the IBM AI Engineering Professional Certificate is an excellent starting point. Offered on Coursera and taught by IBM experts, this program is designed to get you job-ready. It covers essential AI engineering skills through a series of 13 courses, giving you the hands-on experience that hiring managers want to see. You can expect to complete the program in about four months by studying around 10 hours per week. Once you finish, you’ll receive a shareable certificate and an official IBM Badge, which are perfect for adding to your resume and LinkedIn profile to showcase your new qualifications.
Microsoft Azure AI Engineer Associate
For professionals who work within the Microsoft ecosystem, the Microsoft Azure AI Engineer Associate certification is a must-have. This credential proves you can design and build sophisticated AI solutions using Microsoft Azure AI services, including Azure AI Search and Azure OpenAI. It’s considered an intermediate-level certification, making it ideal for those with some existing knowledge who want to formalize their expertise. To maintain the certification, you’ll need to pass a free online assessment every 12 months. This renewal process ensures your skills remain current with Azure’s evolving platform, signaling to employers that you’re committed to staying on top of your game.
Google Cloud Professional Machine Learning Engineer
The Google Cloud Professional Machine Learning Engineer certification is geared toward professionals who design, build, and productionize ML models using Google Cloud technologies. What makes this certification stand out is its focus on the entire machine learning lifecycle. It validates your ability to handle everything from data preparation and model training to deployment and monitoring. Earning this credential shows that you have a comprehensive understanding of how to bring an ML project to life in a real-world business environment. It’s a powerful way to demonstrate your end-to-end expertise to potential employers who rely on the Google Cloud Platform for their data science and analytics needs.
AWS Certified Machine Learning - Specialty
Given Amazon Web Services' massive share of the cloud market, holding an AWS certification is always a smart career move. The AWS Certified Machine Learning - Specialty is a highly regarded credential that validates your expertise in creating, training, tuning, and deploying ML models on the AWS platform. This certification covers a broad range of critical topics, including data engineering, exploratory data analysis, modeling, and implementation. As a "Specialty" level certification, it’s designed for individuals who already have some practical experience with AWS and want to prove their deep knowledge in the machine learning domain, making you a valuable asset to any team building on AWS.
Databricks Certified Generative AI Engineer Associate
With the explosion of interest in generative AI, this certification is incredibly timely. The Databricks Certified Generative AI Engineer Associate credential proves your ability to build and deploy solutions using large language models (LLMs) with Databricks tools. Passing the exam demonstrates that you can create powerful Retrieval-Augmented Generation (RAG) applications and LLM chains—skills that are in high demand. This certification is perfect for engineers who want to specialize in the cutting-edge field of generative AI. It signals to employers that you are equipped with the latest knowledge to build innovative AI products and features, putting you at the forefront of one of tech's most exciting areas.
Skills You'll Validate with an AI Certification
Earning an AI certification does more than just add a new line to your resume; it provides concrete proof of your skills to hiring managers. It shows you’ve put in the work to master specific, in-demand competencies that companies are actively searching for. When a recruiter sees a certification from a reputable provider, they have a clear, standardized benchmark of your abilities. This validation can be the key to standing out in a competitive job market and landing interviews for top-tier AI engineering roles. Let’s break down the core skills you’ll formally demonstrate by getting certified.
Machine learning and deep learning
At the heart of any quality AI certification is a deep focus on the foundational principles of modern artificial intelligence. You’ll prove your command of core concepts like neural networks, deep learning, and the different types of machine learning. More importantly, these programs are designed to keep you current. Many certifications now include extensive training on the latest advancements, such as Generative AI and Large Language Models (LLMs). This shows employers that you’re not just familiar with the theory but are ready to work with the cutting-edge technologies that are shaping the industry right now.
Key programming languages and frameworks
A certification validates your hands-on ability to use the essential tools of the trade. You’ll demonstrate proficiency in key programming languages and the frameworks that bring AI models to life. Coursework and exams often require you to build applications using popular libraries like PyTorch, TensorFlow, Keras, and Scikit-learn. You’ll also get experience with powerful tools like Apache Spark for big data processing and Hugging Face or LangChain for working with LLMs. This practical experience signals to employers that you can integrate seamlessly into their development environment and start contributing from day one.
AI system design and deployment
Building a model is one thing, but getting it to work in a real-world production environment is another challenge entirely. Certifications from major cloud providers like Microsoft and Google specifically test your ability to design, build, and deploy scalable AI solutions. You’ll learn how to use services like Azure AI or Google Cloud AI Platform to manage the entire lifecycle of an AI project. This skill is incredibly valuable because it shows you can think beyond the algorithm and understand what it takes to deliver a functional, reliable AI system that provides real business value.
Data processing and model optimization
Effective AI engineering relies on a solid foundation of data. Certifications confirm your ability to manage and process the data that fuels AI models. You’ll be tested on your skills in planning an AI solution, preparing data, and developing systems for specific applications like natural language processing (NLP) or computer vision. This includes understanding how to interpret text and speech, optimize model performance, and ensure the data pipeline is efficient and robust. It’s a critical skill set that shows you can handle the practical, data-intensive work required in any serious data science role.
AI ethics and responsible practices
As AI becomes more integrated into our lives, the ability to build ethical and fair systems is no longer a "nice-to-have"—it's a necessity. Top certification programs now include modules on "responsible AI," teaching you how to develop and deploy solutions ethically. This covers critical topics like fairness, transparency, privacy, and security. Having a certification that includes this training is a major differentiator. It tells potential employers that you are a thoughtful engineer who understands the societal impact of your work and is committed to building AI that is safe and trustworthy.
What's the Investment? Cost and Time Breakdown
Getting certified is an investment in your career, and that means putting in both time and money. It’s smart to know what you’re signing up for before you commit. The costs often go beyond the initial exam fee to include study materials and long-term renewals. The time you'll need to prepare can also range from a few weeks to several months, depending on the program and your current skill level. Planning for these factors will help you choose a certification that fits your budget and schedule, setting you up for success. Let's break down the financial and time commitments you can expect.
A look at certification pricing
The cost of an AI engineer certification can vary quite a bit. For example, the Databricks Certified Generative AI Engineer Associate exam has a registration fee of $200. On the higher end, a program like the Certified Artificial Intelligence Engineer (CAIE™) costs $749, though this often includes all your study materials. Some certifications also offer cost-effective ways to stay current. The Microsoft Certified: Azure AI Engineer Associate lets you renew your credential every year with a free online assessment. Always check what’s included in the price to avoid any surprises down the road.
How much time you'll need to study
The time you'll need to set aside for studying depends on the certification's depth and your existing knowledge. Most programs provide a recommended timeline to guide you. For instance, the IBM AI Engineering Professional Certificate on Coursera suggests studying for 10 hours per week for about four months. Other programs, like the CAIE™, estimate a completion time between four and 25 weeks with a study schedule of 8-10 hours per week. Think honestly about your schedule and how many hours you can realistically dedicate. A steady, consistent study plan is often more effective than trying to cram everything in at the last minute.
Retake policies and renewal costs
Certifications aren't a one-and-done deal. They require maintenance to stay valid, which ensures your skills remain current in the fast-evolving field of AI. Renewal policies differ between providers. Microsoft requires you to renew your Azure AI Engineer certification annually, but you can do so for free with an online assessment. Others, like the Databricks certification, are valid for two years. The ARTiBA Artificial Intelligence Engineer (AiE®) certification lasts for three years before it needs renewal. Always check the fine print on validity periods and renewal costs to understand the long-term value and commitment of your credential.
What You Need Before You Start
Jumping into a certification program is exciting, but it’s best to go in with a solid foundation. Think of it like this: you wouldn’t start building a house without laying the groundwork first. The same principle applies here. Having the right background and skills not only makes studying for the exam easier but also ensures you can apply what you learn in a real-world job. Let’s walk through the key things you’ll want to have in your toolkit before you get started.
Your educational background
Most successful AI engineers start with a degree in computer science, data science, engineering, or a similar field. The main reason for this is that these programs are designed to build your core mathematical and analytical skills, which are the bedrock of artificial intelligence. If your degree is in a different area, that’s okay—what matters most is having that strong quantitative foundation. You can always fill in the gaps with online courses or self-study to strengthen your understanding of algorithms, data structures, and calculus. The goal is to be comfortable with concepts that will come up again and again as you build and train AI models.
Essential programming and technical skills
On the technical side, proficiency in certain programming languages is non-negotiable. Python is the undisputed leader in the AI space, so you’ll want to be very comfortable with it. Experience with other languages like C# can also be valuable. Beyond just writing code, you’ll need to be familiar with machine learning frameworks like TensorFlow or PyTorch. You should also understand how to use tools like REST APIs and SDKs to build practical AI solutions for things like natural language processing, image recognition, and generative AI. These are the skills you’ll see listed in nearly every AI job description.
Recommended prep paths
If you’re just starting out, you don’t have to jump straight into an advanced professional certification. A great first step is to take an introductory course to get your bearings. Programs like the Microsoft Certified: Azure AI Fundamentals certification are designed for beginners and cover the basics without getting too deep into the weeds. For a more structured approach, you could consider something like the IBM AI Engineering Professional Certificate on Coursera. It offers a clear roadmap and a realistic timeline, taking about four months to complete if you study 10 hours a week. These paths help build your confidence and prepare you for more advanced material.
Is an AI Certification Right for You?
Deciding whether to pursue an AI certification really comes down to your personal career path and what you hope to achieve next. It’s not a magic ticket, but for many professionals, it’s a strategic move that can open doors and clarify their expertise. Whether you're breaking into the field, leveling up your current role, or just starting your career, a certification can serve a different, valuable purpose. Let's look at a few common scenarios to see if one of them sounds like you.
For career changers entering the AI field
If you're looking to pivot into AI from a different industry, a certification can be a game-changer. It provides a structured learning path and a credential that validates your new skills to hiring managers. With the AI job market growing so quickly, a certification shows you’re serious about the transition and have the foundational knowledge to succeed. It helps bridge the gap between your past experience and your future in AI, giving you the confidence and the qualifications to land your first role in this exciting and fast-paced field.
For software engineers adding AI skills
As a software engineer, you’ve probably noticed that AI is no longer a separate discipline—it’s becoming part of the toolkit. AI is increasingly used as a support tool in daily development workflows, and having a formal certification can set you apart. It demonstrates that you’re not just keeping up with trends but are actively building the skills to integrate AI into your work. This credential formalizes your expertise, making you a more versatile and valuable member of any engineering team and preparing you for the future of coding careers.
For data professionals looking to specialize
For data scientists, analysts, and other data pros, an AI certification is a powerful way to specialize. You already know how to work with data; this next step is about learning to build intelligent systems that use that data to create business value. A certification can deepen your understanding of specific AI use cases and how they can augment human capabilities, moving you from a data expert to an AI strategist. It’s the perfect way to focus your career on the cutting edge of machine learning and artificial intelligence.
For recent graduates seeking an edge
Entering the job market as a recent graduate can be tough, and an AI certification can give you a significant advantage. While your degree provides a strong theoretical foundation, a certification offers practical, hands-on experience with the tools and platforms employers use every day. Completing a program like the IBM AI Engineering Professional Certificate proves you have job-ready skills and initiative. It signals to hiring managers that you can start contributing from day one, making you a much more attractive candidate in a competitive field.
How an AI Certification Can Advance Your Career
Earning an AI certification is more than just adding another line to your resume—it’s a strategic move that can shape your entire career path. In a field as dynamic and competitive as artificial intelligence, a certification acts as a clear, verifiable signal to employers that you have the specific skills they need. It shows you’re not only knowledgeable but also dedicated to keeping your skills sharp and relevant.
Think of it as a third-party endorsement of your abilities. While a degree gives you a foundational understanding and a portfolio shows what you can build, a certification proves you’ve mastered a particular technology, platform, or methodology according to industry standards. This can be the deciding factor that sets you apart from other talented candidates and helps you land one of the top AI engineering jobs on the market. It demonstrates a commitment to your craft that hiring managers notice.
Higher salary and more job opportunities
Let’s talk about one of the most compelling reasons to get certified: the impact on your wallet and your career options. Earning a respected AI certification can directly lead to a higher salary and open doors to more job opportunities. Why? Because it validates your expertise in high-demand areas. Companies are actively searching for professionals who can prove they have the skills to build, deploy, and manage complex AI systems, and they are willing to pay a premium for that assurance.
A certification can also help your resume get noticed by recruiters and hiring managers. It serves as a quick filter, showing that you meet a certain baseline of knowledge and competence. This is especially helpful if you're looking to move into a more specialized role within machine learning or data science. By proving your skills upfront, you position yourself as a top-tier candidate ready to tackle advanced challenges.
Greater professional credibility and recognition
A certification from a well-known provider like Google, Microsoft, or IBM lends immediate credibility to your professional profile. It shows that your skills have been tested and verified against industry benchmarks. This recognition is invaluable, as it proves you not only understand core AI concepts but can also apply them to solve real-world business problems. It’s one thing to say you know how to work with a specific AI framework; it’s another to have the official credential to back it up.
This added credibility can make a significant difference in interviews and even in your current role. It builds trust with colleagues, managers, and clients, positioning you as a go-to expert on your team. This kind of professional recognition is a key ingredient for long-term career growth, helping you take on more significant projects and leadership responsibilities. It reflects the same kind of specialized expertise that we value in our own hiring solutions.
A clear path for career advancement
If you’re just starting out or looking to specialize, a certification can provide a structured roadmap for your career. Many certification programs are designed to guide you through a specific learning path, helping you build skills progressively and fill any knowledge gaps. This is particularly useful for recent graduates or professionals transitioning from other tech fields who want to target roles like AI Developer or Machine Learning Engineer.
This structured approach helps you move from a generalist to a specialist, which is often where the most exciting and lucrative opportunities are. Whether you want to focus on Data Infrastructure & MLOps or another niche, a certification program can give you the focused knowledge needed to make that leap. It’s not just about landing your next job—it’s about building a foundation for the job after that.
How to Choose the Right AI Certification
With so many options available, picking the right AI certification can feel overwhelming. The best choice isn't the most popular or expensive—it's the one that fits your unique background and professional ambitions. Think of it as a strategic investment. To make the right call, you need to look at where you are now, where you want to go, and which programs have the industry respect to help you get there. Breaking it down this way makes the decision much clearer.
Assess your current skill level
Before you sign up for a certification, it’s important to do an honest self-assessment. Most successful AI engineers start with a solid foundation, often a degree in computer science or a similar field that builds strong math and analytical skills. You should be comfortable with programming languages like Python and have some experience with machine learning frameworks like TensorFlow or PyTorch. If you’re still building these fundamentals, you might want to focus on foundational courses before tackling an advanced certification. This ensures you get the most value out of the program.
Align certifications with your career goals
What do you want to achieve with this certification? Are you looking to land your first job in AI or aiming for a more specialized role? Your answer will guide your choice. For instance, the IBM AI Engineering Professional Certificate is designed to make you job-ready by focusing on practical, hands-on skills. On the other hand, a credential like ARTiBA’s AiE® certification emphasizes applying AI concepts to solve business problems, which might be better for a strategic role. Look at the AI jobs you’re interested in and see which certifications they mention most often.
Evaluate providers and their industry standing
The organization offering the certification matters. A credential from a tech giant like Microsoft, Google, or IBM carries significant weight with employers because it’s associated with industry-standard tools. For example, the Microsoft Certified: Azure AI Engineer Associate credential validates your ability to build AI solutions using Microsoft Azure services. When comparing programs, look into the provider’s reputation. Do they have strong ties to the AI industry? Are their instructors recognized experts? Choosing a certification from a respected provider gives you more than just a piece of paper—it gives you credibility.
Common Certification Mistakes to Avoid
Getting certified is a fantastic step, but it's just one piece of your career puzzle. It’s easy to fall into a few common traps that can limit the return on your investment of time and money. Think of a certification as a key that can open doors—it gets you into the room, but you still have to make a great impression once you're there. The most successful professionals see certifications as a way to structure their learning and validate their skills, not as a final destination. It’s a signal to employers that you’re serious about your craft and have a verified baseline of knowledge.
However, the real value comes from how you leverage that credential. It’s about being strategic and pairing your new certificate with practical experience and a clear understanding of what employers are looking for. By focusing on genuine skill development, you’ll be better prepared to not only pass the exam but also to excel in your next AI engineering role. This means going beyond the curriculum, building your own projects, and learning to articulate how your skills can solve business problems. The goal isn't just to add a logo to your LinkedIn profile; it's to build confidence and competence that shines through in interviews and on the job. Let’s walk through the most common mistakes so you can sidestep them and make your certification journey as effective as possible.
Believing a certification is a job guarantee
A certification on your resume can definitely catch a recruiter’s eye, but it’s not a golden ticket to a job. Hiring managers are looking for candidates who can solve real-world problems. A certificate proves you have foundational knowledge, but it doesn’t replace the need for a strong portfolio, hands-on project experience, and solid interview skills. One of the biggest misconceptions about AI is that it's just about knowing a specific tool; in reality, it's a discipline focused on problem-solving. Use your certification to build a project that showcases your abilities, and be ready to talk about how you can apply your skills to create value.
Choosing a certification based on price alone
When you’re investing in your career, it’s tempting to go for the most budget-friendly option. However, not all certifications are created equal. A cheaper, less-recognized credential might not carry the same weight with employers as a more established one from a major tech player like Google, Microsoft, or IBM. Instead of focusing only on cost, evaluate certifications based on their industry recognition, the relevance of their curriculum to your career goals, and the practical skills they teach. The goal is to learn how to apply AI to deliver business value, and the right program will equip you for that.
Skipping essential hands-on practice
Passing a multiple-choice exam is one thing, but building and deploying an AI model is another. Simply memorizing concepts to pass a test won't prepare you for the technical challenges of an AI role. As the saying goes, an AI is only as good as the data it’s trained on—and you’re only as good as the practical experience you have. As you study for your certification, dedicate just as much time, if not more, to hands-on projects. Build things, work with messy datasets, and document your process on GitHub. This practical experience is what will truly set you apart and give you compelling stories to share in job interviews.
How to Prepare for Your Certification Exam
Passing a certification exam isn't just about cramming information. It’s about truly understanding the concepts and knowing how to apply them. A structured approach to your preparation will make all the difference, helping you walk into the exam room with confidence. Let's break down a solid strategy for getting ready.
Effective study strategies and resources
Your first step should be to download the official exam guide from the certification provider. This is your roadmap. It outlines every topic that will be covered, so you can create a study plan that addresses each one. Mix up your resources to keep things interesting—use a combination of video courses, official documentation, and community forums. As you study, it’s a great idea to take practice assessments to get a feel for the exam questions, style, and difficulty. This helps you find areas where you need more study and focus your efforts where they’ll have the most impact.
Using practice tests and hands-on projects
Practice tests are your best friend when it comes to exam prep. They do more than just test your knowledge; they get you comfortable with the question format and time constraints. But don’t stop there. The best way to solidify what you’ve learned is by building something. Many certification programs, like the IBM AI Engineering Professional Certificate, are designed so you’ll build a portfolio of projects to show off your skills to future employers. This hands-on work is what truly proves your capabilities and will make your profile stand out when you start applying for AI engineering jobs.
Gaining practical experience as you learn
Certification providers know that real-world experience is invaluable. In fact, some, like Databricks, suggest you have at least six months of hands-on experience building generative AI solutions before even attempting their exam. If you’re not getting this experience in your current role, create it for yourself. Work on personal projects, contribute to open-source AI models, or find freelance gigs. This practical application is crucial because it bridges the gap between knowing a concept and knowing how to use it to solve a real problem—a key skill every company looks for in an AI Engineer.
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Frequently Asked Questions
I already have a computer science degree. Do I still need an AI certification? Think of them as two different tools for your career toolkit. Your degree gave you a fantastic theoretical foundation in concepts like algorithms and data structures. A certification, on the other hand, proves you have practical, hands-on skills with the specific platforms and frameworks that companies are using right now, like Azure AI or Google Cloud. They complement each other perfectly, showing employers you have both the deep knowledge and the job-ready abilities to make an impact.
With so many options, which AI certification is the best one to get? There isn't a single "best" certification for everyone. The right one for you depends entirely on your career goals. A great way to decide is to look at job descriptions for the roles you're most interested in. If you see a lot of companies asking for experience with AWS, then the AWS Certified Machine Learning credential is a smart move. If you want to focus on generative AI, the Databricks certification is a timely choice. Let your career aspirations guide your decision.
How do I keep my certification relevant when AI technology changes so quickly? That's a great question, and it's something certification providers have thought about. Many top credentials, like the Microsoft Azure AI Engineer Associate, require you to pass a free online assessment every year to keep your skills current. This process ensures your knowledge evolves with the platform. Think of your certification not as a one-time achievement, but as the start of a commitment to continuous learning, which is essential for a long-term career in AI.
Can a certification help me land a job if I don't have any professional AI experience? A certification on its own isn't a job guarantee, but it's a powerful tool for breaking into the field. Its real value comes from the process of earning it. As you study, you should be building hands-on projects that you can add to your portfolio. This gives you concrete examples of your work to show hiring managers and compelling stories to share during interviews. It helps prove you can apply your knowledge to solve real problems, which is exactly what employers are looking for.
Should I focus on a vendor-specific certification (like AWS or Google Cloud) or a more general one? This depends on the kind of flexibility you want in your career. A vendor-specific certification is incredibly valuable if you want to work within a particular tech ecosystem, as many companies are built on platforms like AWS, Azure, or Google Cloud. A more general certification, like the IBM AI Engineering Professional Certificate, can provide a broader foundation that's applicable across different environments. A good strategy is to research companies you'd love to work for and see which technologies they use most.
