Artificial intelligence (AI) has moved past the experimentation phase. Companies across every industry are now deploying AI in production, and they need people who can build, ship, and maintain it. If you’re exploring a career in AI, or already working in software engineering, data science, or a related technical field and considering a move, you could be stepping into one of the most in-demand and highest-paying corners of tech.
This guide breaks down what AI engineers actually do, how much they earn, the skills employers expect, and how AI engineer jobs compare to machine learning engineer jobs, AI developer roles, and generative AI engineer positions. You’ll also find a practical roadmap for entering the field, whether you’re searching for entry-level AI engineer jobs or looking for remote AI engineer jobs that fit your current lifestyle.
KEY TAKEAWAYS
- AI engineering is now the highest-paid specialization in software engineering, with base salaries commonly ranging from $145,000 to $310,000 and senior total compensation frequently exceeding $300,000.
- The Bureau of Labor Statistics projects 35% growth for data scientists and 22% growth for computer and information research scientists through 2035, both far outpacing the 3% average across all occupations.
- Machine learning engineer jobs pay an average of $134,000 to $176,000 and focus on model training and validation, while AI engineer and AI developer roles lean toward deploying and integrating already-built models.
- A bachelor’s degree remains the standard entry point. A strong applied portfolio often carries more weight than certifications alone.
WHAT DOES AN AI ENGINEER DO?
An AI engineer builds systems that let software perform tasks that normally require human judgment, such as recognizing images, understanding language, making predictions, and generating content. Day to day, that involves writing code, deploying machine learning models into production applications, integrating AI capabilities like large language models (LLMs) into existing products, and monitoring how those systems perform once real users start relying on them.
AI engineering has shifted from training models to deploying them. Where AI engineers once spent most of their time training models from scratch, most now spend more of it deploying and customizing pretrained models, building retrieval-augmented generation (RAG) pipelines, fine-tuning foundation models for a specific business problem, and shipping AI-powered features that hold up in production. Instead of just hiring researchers, companies are looking for engineers who can turn powerful, pretrained models into reliable products.
WHY ARE AI ENGINEERS IN HIGH DEMAND?
AI engineer ranks as the fastest-growing job title in the United States, according to LinkedIn’s Jobs on the Rise 2026 report. That growth traces back to a simple shift: businesses spent 2023 through 2025 experimenting with AI, and now they’re integrating it into core operations, which calls for a more advanced and specialized skill set than most existing teams have in-house.
Government data backs up the demand. The Bureau of Labor Statistics projects that data scientist employment will grow 35% through 2035, and computer and information research scientist employment will grow 22% over the same period, both far outpacing the 3% average growth projected across all occupations. AI engineer isn’t a standalone category the Bureau tracks yet, but it draws from exactly these fast-growing occupational groups.
THE AI TALENT SHORTAGE AND WHAT IT MEANS FOR JOB SEEKERS
Employers are struggling to fill AI roles at a scale most other tech disciplines haven’t seen. A 2026 survey, which polled 39,000 employers across 41 countries, found that AI skills have become the hardest capability to hire globally, ahead of traditional engineering and IT skills. Research estimates global demand for AI talent outpaces qualified supply by more than 3 to 1.
For job seekers, this shortage cuts in your favor. Companies are widening their search criteria, increasing starting pay, and investing in upskilling current employees rather than waiting for the perfect resume. If you have a solid technical foundation and are willing to build AI-specific skills, you’re entering the market at a moment when employers need you more than you might think.
TYPES OF AI ENGINEER JOBS
AI engineer functions as an umbrella term that covers several distinct roles. Employers use these titles inconsistently, so understanding what each one typically involves helps you target the right jobs and negotiate from an informed position.
Keep in mind that the salary figures below reflect national averages and vary significantly based on location, years of experience, company size, and industry, so use them as a general benchmark rather than a guaranteed number for any specific role or market.
AI SOFTWARE ENGINEER
An AI software engineer focuses on the software engineering side of AI systems, such as building the applications and infrastructure that let AI models function inside a real product. This role is more closely related to traditional software engineering than to research, with AI as one component of a larger system rather than the entire job.
Average Salary: $153,777
Required Education: A bachelor’s degree in computer science, software engineering, or a related field is standard. Certifications in cloud platforms (AWS, Azure, or Google Cloud) and applied machine learning add credibility, though hands-on project experience typically matters more to hiring managers than certifications alone.
Core Technical Skills: Proficiency in Python or a similar language, experience integrating APIs, familiarity with cloud infrastructure, and working knowledge of machine learning frameworks like PyTorch or TensorFlow.
Core Soft Skills: Cross-functional collaboration with product and data teams, clear technical communication, and comfort operating in fast-changing environments where tools and best practices can shift quickly.
ARTIFICIAL INTELLIGENCE ENGINEER
Used as a general title, an artificial intelligence engineer designs, builds, and deploys AI systems end to end, from selecting the right model architecture to putting a finished system into production. This is the most commonly used title in job postings and tends to blend elements of both software engineering and machine learning engineering.
Average Salary: $145,000 to $310,000
Required Education: Most employers expect a bachelor’s degree in computer science, data science, or a related field. A master’s degree strengthens candidacy for research-adjacent or senior roles. Relevant certifications in machine learning or cloud AI services can help candidates without a traditional CS background stand out.
Core Technical Skills: Machine learning fundamentals, Python programming, experience with LLMs and RAG architecture, MLOps and model deployment practices, and cloud platform experience.
Core Soft Skills: Problem-solving under ambiguity, the ability to translate business problems into technical solutions, and adaptability, since the tools and best practices in this field change faster than in most other engineering disciplines.
GENERATIVE AI ENGINEER
A generative AI engineer specializes in systems that create new content, including text, images, audio, code, or video. This also involves fine-tuning foundation models, building applications on top of large language models, and designing evaluation frameworks to keep AI-generated output reliable and safe for production use.
Average Salary Range: $115,864
Required Education, Certifications, or Training: A bachelor’s degree in computer science or a related technical field is the baseline. Because this specialty is so new, hands-on portfolio work with LLMs, fine-tuning, and RAG pipelines often carries more weight than formal credentials.
Core Technical Skills: Prompt engineering and evaluation, LLM fine-tuning techniques, RAG architecture, vector databases, and familiarity with frameworks like LangChain.
Core Soft Skills: Creative problem-solving, since generative AI use cases are still being invented, along with careful judgment around output quality, safety, and bias.
MACHINE LEARNING ENGINEER
A machine learning engineer designs, trains, and validates machine learning models, then works with data and software teams to move those models from experimentation into production. This role traditionally involves more statistics, data preparation, and model architecture work than an AI engineer role focused on deployment.
Average Salary: $187,529
Required Education: A bachelor’s degree in computer science, statistics, mathematics, or a related quantitative field is standard, with many employers preferring a master’s degree for mid-level and senior roles. Certifications in machine learning (such as TensorFlow Developer or AWS Machine Learning Specialty) can support candidates transitioning from adjacent fields.
Core Technical Skills: Strong statistics and applied math foundation, Python and machine learning libraries (scikit-learn, PyTorch, TensorFlow), data pipeline and feature engineering experience, and model evaluation and monitoring.
Core Soft Skills: Analytical rigor, patience for iterative experimentation, and the ability to explain model behavior and limitations to non-technical stakeholders.
AI DEVELOPER
An AI developer builds applications and features that incorporate AI capabilities, often working closer to the product and user experience than an engineer focused purely on model performance. This title frequently overlaps with AI software engineer, though it tends to emphasize application development over infrastructure.
Average Salary: $151,702
Required Education: A bachelor’s degree in computer science or software engineering is common, though this role has more flexibility for candidates entering through bootcamps or self-directed learning, provided they can demonstrate applied AI project experience.
Core Technical Skills: Application development fundamentals, API integration, working knowledge of AI/ML libraries, and version control and deployment practices.
Core Soft Skills: User-focused thinking, comfort working closely with product and design teams, and the ability to balance technical constraints against user experience goals.
ENTRY-LEVEL AI ENGINEER JOBS
Entry-level AI engineer roles typically involve supporting more senior engineers on model deployment, data preparation, testing, and monitoring tasks, while building toward independent ownership of AI features and systems.
Average Salary: $69,362
Required Education: A bachelor’s degree in computer science or a related field remains the most common path in. Candidates without a CS degree can strengthen their case with completed coursework, certifications, and a portfolio of applied AI projects.
Core Technical Skills: Solid programming fundamentals (typically Python), basic machine learning concepts, familiarity with at least one ML framework, and the ability to work with data pipelines.
Core Soft Skills: Willingness to learn quickly, coachability, and honesty about what you do and don’t yet know. This field moves fast, and few hiring managers expect a new hire to have it all mastered on day one.
REMOTE AI ENGINEER JOBS
Remote AI engineer roles carry out the same responsibilities as their in-office counterparts, from model development to deployment and monitoring, executed from anywhere with sufficient time zone overlap with the employer’s team.
Average Salary: $180,173
Required Education: Education and certification expectations match those of in-office AI engineer roles. Remote candidates benefit from a strong portfolio and clear documentation of past work, since employers can’t rely on in-person observation to evaluate fit.
Core Technical Skills: The same core AI engineering skill set, plus strong asynchronous collaboration tools experience (version control, project management platforms, and written documentation practices).
Core Soft Skills: Self-direction, proactive communication, and the discipline to stay visible and aligned with a distributed team without in-person check-ins.
AI ENGINEER VS. OTHER AI CAREERS
With so much overlap in titles and responsibilities, it helps to see these roles side by side and understand where each one actually differs. This section focuses on the two comparisons job seekers ask about most: AI engineer vs. machine learning engineer and AI engineer vs. AI developer.
AI ENGINEER VS. MACHINE LEARNING ENGINEER
A machine learning engineer typically spends more time selecting algorithms, training models, and tuning performance against a specific metric. An AI engineer more often works on taking already-built or pretrained models (including LLMs) and integrating them into applications that solve a business problem.
In practice, many job postings use these titles interchangeably, so read the actual responsibilities listed rather than relying on the title alone.
AI ENGINEER VS. AI DEVELOPER
AI engineer and AI developer roles overlap even more than AI engineer and machine learning engineer. Where a distinction exists, “engineer” titles tend to skew toward system design, infrastructure, and model performance, while “developer” titles skew toward building the application layer and user-facing features that incorporate AI. Company size also plays a role. Smaller companies and startups often use “developer” more loosely to cover both types of work.
WHICH AI CAREER PATH IS RIGHT FOR YOU?
Your strongest starting point usually depends on your existing background:
- If you come from software engineering, an AI engineer or AI developer path lets you build on application development skills while layering in AI-specific tools.
- If you come from data science or statistics, machine learning engineer roles let you apply your modeling background to production systems.
- If you’re drawn to cutting-edge, fast-moving work and enjoy experimenting with new tools, generative AI engineer roles offer the most exposure to LLMs and foundation models.
- If you want maximum flexibility in where and how you work, remote AI engineer roles and AI developer positions tend to offer the broadest range of opportunities.
| Role | Primary Focus | Average Salary | Best Fit For |
| AI Software Engineer | -Build the applications and infrastructure that let AI models function inside a real product | ~$153,400 | Software engineers adding AI skills |
| AI Engineer | -Design, build, and deploy AI systems end to end | ~$145,000-$310,000 | Generalists comfortable across ML and software engineering |
| Generative AI Engineer |
-Create new content, including text, images, audio, code, or video -Fine-tune foundation models, build applications on top of LLMs, and design evaluation frameworks to keep AI-generated output reliable |
~$116,000 | Engineers drawn to the newest, fastest-moving AI work |
| ML Engineer | -Design, train, and validate MLMs -Work with data and software teams to move those models from experimentation into production | ~$187,000 | Data scientists and statisticians moving into engineering |
| AI Developer |
-Build applications and features that incorporate AI capabilities -Work closer to the product and user experience |
~$152,000 | Developers who want product-focused AI work |
HOW TO BECOME AN AI ENGINEER
Whether you’re starting from scratch or transitioning from an adjacent technical field, here’s a practical path to becoming a competitive AI engineering candidate.
- Build your technical foundation. Get comfortable with Python, statistics, and core computer science concepts (data structures, algorithms) before adding on AI-specific skills.
- Learn machine learning fundamentals. Understand how models are trained, evaluated, and deployed, even if you plan to specialize in a role that focuses more on application than research.
- Specialize. Choose a direction, whether that’s generative AI, MLOps, computer vision, or another niche, based on your interests and the market demand in your area.
- Build a portfolio. Complete projects that demonstrate applied skills, not just coursework, since hiring managers weigh demonstrated work heavily.
- Gain production experience. Look for internships, contract work, or opportunities within your current job to work on real AI systems, not just personal projects.
- Apply strategically. Target roles that match your actual experience level and be ready to explain trade-offs you made in your project work.
EDUCATION AND DEGREE OPTIONS
A bachelor’s degree in computer science, data science, mathematics, or a related quantitative field remains the most common entry point. A master’s degree strengthens candidacy for research-oriented or senior roles, though it isn’t strictly required for many engineering-focused positions. Candidates without a traditional CS degree can enter through rigorous bootcamps or self-directed study, provided they can back that education with real project work.
RECOMMENDED CERTIFICATIONS
Certifications won’t replace hands-on experience, but they can help validate specific skills, especially for career changers. Commonly recognized options include cloud platform certifications (AWS Certified Machine Learning, Google Cloud Professional Machine Learning Engineer, Microsoft Azure AI Engineer Associate) and framework-specific credentials like TensorFlow Developer Certificate.
BUILDING AN AI PORTFOLIO
Use your AI portfolio to show more than finished models. Document the problem you were solving, the approach you took, the trade-offs you considered, and how you evaluated results. Include at least one project that goes beyond a tutorial, ideally something you deployed and can discuss in terms of real-world constraints.
GAINING HANDS-ON EXPERIENCE
If your current job doesn’t involve AI work, look for ways to bring AI into it, such as automating a repetitive process, building a small internal tool, or proposing a pilot project. Open-source contributions, Kaggle competitions, and freelance projects also count as legitimate experience.
TIPS FOR LANDING YOUR FIRST AI ENGINEERING JOB
Target companies and teams where your existing background gives you an advantage rather than competing head-on with candidates who have years of specialized AI experience. Be direct about what you know and what you’re still learning. Hiring managers in this field expect candidates to be honest about the edges of a skill set that’s evolving quickly. Apply to a mix of AI-specific titles and adjacent software or data roles that involve AI as part of a broader scope, since the title alone won’t always reflect the actual work.
SEE HOW YOUR SALARY COMPARES IN NEW ENGLAND’S TECH MARKET
Curious to see the current market rate for your role, or want to know if your offers are competitive? Download Alexander Technology Group’s Technology & IT in New England Salary Guide for region-specific salary data across key tech and IT roles in Massachusetts and New Hampshire.
FAQs
How long does it take to become an AI engineer?
Most career changers take 12 to 24 months to become job-ready AI engineers, depending on their starting point. Someone coming from software engineering can often transition in under a year by adding AI-specific skills to an existing foundation. Someone starting from scratch typically needs 18 to 24 months to build programming fundamentals, machine learning knowledge, and a portfolio strong enough to compete for entry-level roles.
What’s the difference between an AI engineer and a data scientist?
A data scientist focuses on analyzing data and building models to uncover insights or predictions, often working with statistics and business stakeholders to answer specific questions. An AI engineer focuses on building and deploying the software systems that put those models, or pretrained models like LLMs, into production applications. Data science leans analytical; AI engineering leans toward software engineering and systems design.
Do AI engineers need a master’s degree or PhD?
No, most AI engineering roles don’t require a master’s degree or PhD. A bachelor’s degree in computer science or a related field satisfies the typical requirement for engineering-focused positions. PhDs are more common among AI research scientists working on novel algorithms or theoretical advances, not among engineers focused on building and deploying applied AI systems.
Can you become an AI engineer without a technical background?
Yes, you can become an AI engineer without a technical background, but it takes deliberate effort to build a foundation first. Professionals coming from non-technical fields typically need to complete coursework or a bootcamp covering programming and machine learning basics, then build applied projects that demonstrate real skill. Domain expertise from a non-technical background, such as healthcare or finance, can become a differentiator once paired with solid technical fundamentals.
Which industries hire the most AI engineers?
Technology, financial services, healthcare, and retail currently hire the largest share of AI engineers, driven by demand for fraud detection, personalization, clinical decision support, and automation. Manufacturing and logistics are growing quickly in the space too, as companies apply AI to predictive maintenance and supply chain optimization. Tech companies still offer the highest concentration of open roles and the highest average pay.
