AI career paths: 2025 job guide
A list of five AI career paths you can pursue in 2025, with roles, salaries, skills, and portfolio ideas to help you launch and grow your career.
Oct 28, 2025 • 10 Minute Read
Basic AI skills are now table stakes for most professionals, with 95% of organizations using these as a hiring factor. But while most people can enter a prompt into ChatGPT, professionals with deep AI expertise are still rare and in demand.
In this guide, you’ll learn about five major AI career paths, including what they involve, who thrives in them, expected salaries, skills, and hands-on portfolio ideas.
Note: All average salary data below is in USD sourced from Glassdoor as of October 2025. Salary ranges will differ depending on geography, company size, and individual experience.
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1. ML engineering
Machine learning engineers (MLE) are specialists at designing, training, and deploying ML models to solve business problems. In 2025, this field is increasingly considered a subset of software engineering, as the availability of pretrained models has made AI more accessible. The main difference is that MLEs have a deeper knowledge of machine learning algorithms and techniques than traditional software engineers (SWEs).
MLEs are highly collaborative, working with both data scientists and SWEs to meet business needs. Beyond models and software, there is a lot of working with data pipelines and dependencies.
Who would thrive
People who enjoy being hands-on problem solvers, but also love math, data, collaborating with others, and software development. Even though the role involves programming, people from backgrounds in math, statistics, or science can thrive in this field.
Typical salary range
The average salary range in ML engineering is between $127k to $201k per year.
Tools, technologies, and skills to know
Deep Python knowledge: Particularly for ML engineering and data workflows.
Popular ML libraries and frameworks: Look into pandas (The fundamentals and advanced techniques), NumPy, PyTorch, TensorFlow, PySpark, scikit-learn, XGBoost, LightGBM, and Statsmodels.
Databricks and ML lifecycle flows: Studying up on MLflow is a good example.
CI/CD and DevOps practices: Great to know for any engineering role.
Secure deployment protocols: Understand things like OAuth, RBAC.
A debatable amount of math: Some will say you should know linear algebra, multivariate calculus, probability theory, etc. Others say PyTorch abstracts this away for you.
Cloud computing, including Docker and Kubernetes: This will help you with training, data pipelines, deploying applications, etc.
Valued mindsets and soft skills
Continuous learning: AI moves faster than most fields, and there are always new models and solutions to learn about.
Collaboration and teamwork: ML engineering doesn’t happen in a vacuum. You’re solving business problems with and for other people.
Analytical thinking: Being able to break a whole problem down into smaller parts and take an in-depth look is a valuable skill.
Problem solving: Vital for any profession that involves any component of SWE.
Portfolio idea
Try projects like creating a simple image classifier, a sentiment analysis tool, or a movie recommendation system. Use real datasets from Kaggle to get started.
ML engineer vs AI engineer vs DL engineer: Are they the same?
Short answer: Yes. Long answer: Technically, machine learning is a subset of AI, and deep learning (DL) is a subset of ML. You could then argue that an AI engineer has a wider scope than a ML engineer. In reality, all of these are vague titles for a profession with loosely defined role responsibilities*.
* That’s why every career guide (including this one!) should be taken as helpful guidelines more than rules.
How about Applied ML engineer, is that different to a regular MLE?
It’s increasingly common to see the title “Applied ML Engineer” or “Applied AI Engineer” on job listings. However, when you dive into the job duties and requirements, they are often functionally identical to any other MLE role, with a slightly increased focus on microservices and delivering user-facing products.
2. Data science
Data scientists develop, implement and test new theories and processes to empower an organization. They often perform more complex analysis than a data analyst, running experiments and building predictive and prescriptive models.
Who would thrive
Anyone who enjoys intellectual challenges, coding, discovering things that nobody else knows, and constantly learning new things. If you love machine learning and algorithms, data science may also be a fit for you.
Typical salary range
The average salary range in data science is between $121k and $196k per year.
Tools and technologies to know
Key programming languages: Having hands-on SQL experience is considered a baseline requirement for many jobs. Python, R, Scala, and Java are also popular.
Machine learning model learning methods: You should know the difference between all the different approaches, such as supervised and unsupervised.
Popular libraries: Learn Pandas, Matplotlib, NumPy, scikit-learn, plotly, and Pytorch/TensorFlow.
Deep data handling knowledge: You should be intimately familiar with ETL processes, data pipelines, data processing, and data analytics.
Predictive and prescriptive modelling: You’re going to be dealing with models. A lot.
Advanced statistics and algorithms: Math. Just math.
Valued mindsets and soft skills
Pattern recognition and analysis: Once again, if you’re in the field of data, this is an essential soft skill.
Creativity: You’re coming up with theories and solutions, so it helps to think outside the box.
Critical thinking: You need to question the data, your own assumptions, and that of others, otherwise your analysis may lead to uninformed or misinformed decisions.
Continuous learning: Any field in IT requires this, but when you’re involved with machine learning and programming, this is dialled up to eleven.
Data storytelling: There’s no point knowing a secret pattern if you can’t properly share it with others. You need to be able to tell stories with and about data.
Collaboration: You’ll be talking to stakeholders, finding their issue, communicating with others to help implement your models, and sharing results.
Curiosity: The best insights start with the right questions.
Portfolio idea
Build a feature importance analysis on an already trained housing model to determine which features are pulling the most weight in classifying, and identify how accuracy metrics would be negatively impacted by removal of specific features.
3. AI research
AI research scientists push the boundaries of what AI can do so that everyone else, such as data scientists and ML engineers, can then follow. They do this through novel research into algorithms, models, and architectures. Just like other forms of research, they share this by publishing papers, prototypes or patents.
Who would thrive
Naturally curious people who thrive on exploring novel ideas, experimentation, math, and theory.
Typical salary range
The average salary range for an AI research scientist is between $155k and $238k per year.
Tools and technologies to know
High-level tertiary qualifications
Normally, this is the part of the guide where I say “Hey, you don’t need an academic degree to enter this field!” However, with AI research, you almost always do—you’re a research scientist, after all.
A PhD is the most straightforward way to get the skills you need and learn what has been done before, so you can then push the boundaries from there. Much like being a doctor, becoming an AI research scientist and landing this as a job is not a short-term commitment—it can take up to a decade to actually achieve. Your PhD should ideally be in computer science, machine learning, engineering, or a related field.
A strong publication record
A publication record or evidence of research impact in academia or industry is going to be key to landing a job. For jobs with big tech companies, you’ll want to work towards getting into top-tier peer-reviewed machine learning, natural language processing, or information retrieval conferences (e.g., NeurIPS, ICML, ICLR, ACL, KDD, AISTATS).
Deep experience in mathematics
More than the basics, you’re going to need to have a deep understanding of computational math. The good news is that the math won’t be fancy abstract stuff, but actually rather narrow and specific to the field.
Solid experience in machine learning
Particularly areas like LLMs, vision-language models, generative AI, or multi-modal systems. You’ll want to get experience researching, developing, and implementing data mining and deep learning algorithms.
Programming knowledge (Start with Python)
This will be the easiest thing for you to pick up in the grand scheme of things you need to learn. Study up on languages such as Python (essential!), Java, or C++. Also learn how to use TensorFlow and Pytorch, as these are frequently used in research.
Valued mindsets and soft skills
Problem solving: You’re coming up with novel solutions to real world problems.
Analytical thinking: A research staple.
Creativity: Those that can see beyond what exists now will thrive in research.
Teamwork and independent thinking: You’ll need to both work independently and collaboratively depending on the situation.
Continuous learning: Essential to stay ahead of current research and advancements so that you can take it a step further.
Portfolio idea
As mentioned above, your portfolio is going to be your publication record and evidence of research impact. Work at getting published in top-tier AI conferences or journals. Get demonstrable experience contributing to research projects from problem formulation to deployment.
4. MLOps engineering
Machine Learning Operations (MLOps) engineers manage the infrastructure and pipelines that are required for others to train, deploy, and monitor ML models effectively. It is where DevOps meets machine learning, but also requires knowledge and skills in ML and data engineering. As such, MLOps engineer isn’t typically an entry-level position.
How is an MLOps engineer different from an ML engineer?
The difference is the foundation. MLOps is DevOps with a ML focus, while ML engineering is software engineering with a ML focus. Your mileage may vary depending on the company and the reality is a bit more nuanced, but think of this as a general rule of thumb.
Who would thrive
Existing DevOp specialists, data scientists, data engineers, and ML engineers looking to progress their careers. Anyone with a love of solving complex problems across multiple domains and being the glue that holds everything together.
Typical salary range
The average salary range for an MLOps engineer is between $132k and $199k per year.
Tools and technologies to know
AIOps/MLOps: Pretty much a given. Study up on the concepts behind AI operations (AIOps) and machine learning operations (MLOps).
ML engineering fundamentals: Also a given, since knowing how ML engineering works helps you manage the infrastructure and pipelines needed to support this.
Containerization and orchestration: These are important to make sure everything works in production and can scale, so study up on Kubernetes, Docker, Jenkins, and Terraform.
Cloud computing: It’s supremely unlikely you’ll be doing MLOps without cloud computing, so you’ll need to be familiar with at least one of the “big three” (AWS, Azure, or GCP) as well as the relevant tools and services for MLOps (AWS Sagemaker, GCP Vertex AI, or Azure ML).
ML monitoring solutions: Study Prometheus and Grafana.
Feature stores: You’ll be doing a lot of work building and maintaining feature stores for data scientists to use, which tools like Feast or proprietary setups.
Programming: As usual, Python is your go-to language to learn (see what I did there? Yes, I know goto doesn’t exist in Python, shut up.)
Project management tools: Learn the three Gits: Git, GitHub, and GitLab. You should be very familiar with Jira, Confluence, and Slack.
DevOps frameworks and concepts: Since MLOps is applying a lot of DevOps concepts to ML, you’ll want to know these.
Valued mindsets and soft skills
Communication and collaboration: For MLOps to succeed, there needs to be a strong culture of collaboration, and you’ll need to foster and nurture it.
Problem solving: You’ll be putting out fires and solving pain points to keep things moving.
Customer thinking: Growing and developing your ability to understand customer pain is a valuable MLOps skill.
Holistic thinking and project management: You’ll need to be thinking of the whole ML lifecycle, as well as picturing all the moving pieces and how they fit together.
Time management and multitasking: It’s easy to get overwhelmed with things to do in MLOps, so being able to manage yourself and others is a great skill.
Critical thinking: In MLOps, you’re questioning why things are the way they are, and if they can be done better.
Continuous learning: Keeping pace with industry trends, tools, and best practices is essential for MLOps success.
Portfolio idea
Build a system to predict rental data using publicly available data, building an end-to-end automated pipeline that handles tasks like data preprocessing, model training, and evaluation, as well as alerts and error handling.
5. AI policy, ethics, and governance
AI adoption is more than just a technical challenge—you need to account for business and security risks, regulations, and safe and responsible adoption (they’re also never finished). Specialists in this field are subject-matter experts on best-practice AI adoption, compliance and regulatory frameworks. They help decide where, when, and how AI should be adopted. A large part of their role is stakeholder engagement, empathising with customer needs while still mitigating potential project risks.
The titles associated with this career path are many and varied: AI strategy consultant, AI ethicist, responsible technologist specialist. Often AI does not fall into the role title at all, especially when the person in charge of AI governance is also tasked with similar duties, such as cybersecurity governance, risk, and compliance (GRC).
Who would thrive
Anyone who is passionate about responsible AI adoption. Great communications with exceptional stakeholder and risk management skills. Continuous learners who love to learn how others have adopted AI for good or ill, as well as keep up with the latest in AI policy, standards, and technology.
Typical salary range
The average salary range for an AI governance consultant is between $139k and $251k per year.
Tools and technologies to know
AI governance frameworks and regulations: What you need to learn is going to vary depending on the country you’re working in/with. However, you should check out ISO’s standards on AI (particularly ISO 42001), the EU AI Act, and NIST’s AI Risk Management Framework (AI RMF).
Knowledge of AI models and tools: You don’t need to be a hands-on engineer who can build a model—this career is far more focused on soft skills—but having a strong theoretical understanding of how AI works, including LLMs, is extremely beneficial. Of course, the best way to understand how something works is to be hands on, but it’s not necessary for you to understand the best case use of the Random Forest algorithm in order to write AI policy.
Foundational cybersecurity knowledge: There is a lot of overlap between security and AI governance. Besides taking foundational courses and certifications (such as your ISC2 CC, SSCP, or CompTIA Security+), I’d recommend regularly studying up on OWASP top 10 vulnerabilities, particularly for LLMs.
Foundational cloud knowledge: Just like with cybersecurity, AI has a lot to do with cloud computing. By taking a foundational certification in AWS or Azure, you’ll get a crash course in what cloud infrastructure looks like, including important elements for AI such as data pipelines. Since the principles are fairly universal, you can study any of the big three to understand these concepts.
Valued mindsets and soft skills
Stakeholder engagement/management: You’ll be getting buy-in upwards for leadership to follow your vision for responsible adoption, as well as constantly engaging with various business areas to ensure proper AI governance.
Communication skills: Works hand-in-hand with your stakeholder skills, very crucial.
Holistic thinking: You’ll need to be a big-picture thinker who can identify the organization’s wider problems and how AI fits into that.
Risk management: AI comes with many inherent risks, and your job is to identify and mitigate these.
Project management: Implementing AI governance and frameworks are inherently projects, so knowing how to manage these is a valuable skill.
Continuous learning: This is a big one! If you love reading AI news every day, and thrive on the idea of learning all about the latest governance and best practice, you’ll do exceptionally well in this career.
Critical thinking: Asking “Hey, is this the best way to do things?” is baked into the job.
Empathy: Both for working with your internal and external customers, but also just for being the most human person in the room when it comes to thinking about how uncritical adoption can lead to actual human harm.
Portfolio idea
Work on an AI compliance project, such as adopting a popular framework like ISO 42001. Alternatively, you could use an AI audit tool or checklist to assess a model and then publish your results.
Conclusion
While it could be argued that every tech role is becoming an AI role in 2025, these five career paths are great options for anyone who wants to make a career as someone who has deep AI expertise.
No matter what path you choose for your AI career, or even if you choose something entirely different, I wish you the best of luck!
Getting your AI career started
If there’s one common thread that all AI careers share, it’s a need for continuous learning to both get started and stay ahead. One way to get started is to take one of Pluralsight’s beginner, intermediate, and expert AI and ML courses. Since you can sign up for a 10-day free trial with no commitments, it’s a great way to take some professionally authored courses with a set course structure. Why not check them out?
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