Does it feel like every job posting you encounter these days mentions “AI skills” somewhere? It’s not a coincidence: Employers now require AI skills in roughly three times as many job postings as they did two years earlier (2023 vs. 2025). Unique job postings calling for generative AI skills grew from just 55 in January 2021 to nearly 10,000 by May 2025. That’s an over 18,000% increase!
If you’re looking specifically for tech roles related to AI, this is good news. Tech professionals with AI skills are in high demand — but it also means the field is highly competitive. As you search for the ideal role in tech, be sure you can claim the following essential AI skills.
This list draws on recent AI and IT skills reports and job‑postings that highlight three core areas: core modeling skills, production and infrastructure skills, and emerging skills around generative AI and responsible AI use. Together, these reflect what employers now expect from AI‑fluent tech professionals across engineering and data roles.
1. Machine Learning
Machine learning enables systems to learn from data and improve performance over time without being explicitly programmed. It powers predictive and pattern-recognition capabilities across industries. Organizations use machine learning to forecast demand, detect fraud, personalize recommendations, and optimize operations. It is especially valuable when decisions depend on large, evolving datasets.
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2. Deep Learning
Deep learning is a subset of machine learning that uses multi-layer neural networks to analyze complex data such as images, audio, and language. It supports advanced applications like speech recognition, image classification, and generative AI. It is commonly used when traditional machine learning models cannot capture enough complexity.
3. Natural Language Processing (NLP)
Natural language processing enables AI systems to understand, generate, and analyze human language in text or speech form. NLP is used for chatbots, document summarization, sentiment analysis, search optimization, and language translation, enabling organizations to extract insights from large volumes of unstructured text.
4. Computer Vision
Computer vision enables machines to interpret and analyze visual data such as images and video, automating tasks that traditionally require human visual judgment. It is used in quality inspection, facial recognition, medical imaging, autonomous vehicles, and security systems.
5. Python Programming
Python is a widely used programming language that serves as the foundation for most AI and machine learning development. It is used to build models, analyze data, automate workflows, and integrate AI into applications. Its extensive library ecosystem makes it the default language for AI development.
6. Data Engineering
Data engineering focuses on building and maintaining the pipelines and infrastructure that make data usable for AI and analytics. Strong data engineering ensures AI models are trained on clean, reliable, and timely data. It supports everything from real-time analytics to large-scale machine learning systems.
7. AI Model Deployment (MLOps)
MLOps combines machine learning, DevOps, and data practices to deploy, monitor, and maintain AI models in production environments. Organizations use MLOps to ensure models perform reliably after launch, adapt to changing data, and meet governance requirements.
8. Cloud Platforms for AI (e.g., AWS, Azure, GCP)
Cloud platforms provide the infrastructure, tools, and services needed to build, train, and deploy AI systems at scale. Cloud-based AI enables organizations to scale models quickly, manage large datasets, and reduce infrastructure costs. It supports collaboration, automation, and continuous model improvement.
9. Prompt Engineering
Prompt engineering is the skill of designing effective inputs that guide generative AI systems to produce accurate, relevant, and useful outputs. Both tech and non-tech professionals use prompt engineering to improve AI-generated writing, analysis, code, and customer interactions.
Example jobs
10. AI Ethics and Explainability
AI ethics and explainability focuses on ensuring AI systems are fair, transparent, accountable, and understandable to humans. Organizations apply these skills to reduce bias, comply with regulations, and build trust in AI-driven decisions. Explainable AI is especially critical in hiring, finance, healthcare, and public-sector use cases.
Learning artificial intelligence skills doesn’t require a four-year computer science degree, but it is recommended to pursue some formal training to ensure you’re up to date on trends and developments. The most effective learning paths combine foundational knowledge and hands-on practice, and result in credentials that signal readiness to employers.
Start by learning how AI systems work at a conceptual level before diving into any specializations. This includes understanding core terms such as machine learning, neural networks, data pipelines, and model training. Introductory courses and self-guided learning programs help learners develop context without being overwhelmed by technical depth.
Hands-on experience is essential. Learners can build credibility by using AI tools to solve practical problems, such as automating workflows, analyzing datasets, or experimenting with generative AI. Popular learning environments include open-source frameworks such as OpenAI and AI sandbox platforms that allow experimentation without production risk.
Employers increasingly value applied skills over theory alone. This means learning how to clean data, evaluate model performance, deploy solutions, and explain outcomes to non-technical stakeholders. Project-based learning, case studies, and simulations help bridge the gap between learning and real-world use.
Certificates and credentials help learners demonstrate skill progression and commitment to AI literacy. Short-form certificates and professional programs are especially attractive to working professionals because they offer focused learning without requiring a long-term degree commitment. These credentials can support career pivots, internal promotions, or entry into AI-adjacent roles.
For learners pursuing advanced technical or leadership roles in AI, formal degree programs provide depth in modeling, analytics, and system design. While not required for every role, degrees can be valuable for those targeting research-intensive or highly technical positions.
Artificial intelligence is no longer a niche technical specialty, but a foundational capability across industries and career paths. As AI continues to reshape how work gets done, professionals who understand how to work with these technologies will be better positioned to adapt, lead change, and create long-term value.
It is imperative that technical professionals build durable skills, including understanding how AI systems work, knowing how to apply them responsibly, and being able to translate AI-driven insights into real-world impact. Those capabilities will matter well beyond 2026 as tools and platforms evolve.
For professionals looking to build these skills in a formal yet flexible program, the University of San Diego’s Division of Professional and Continuing Education offers the AI for Business Solutions Certificate. Designed for working professionals across industries, the program provides practical, job-relevant learning that helps learners apply AI confidently and ethically in the workplace.
What are the top AI skills for 2026?
The top AI skills for 2026 include machine learning, data engineering, prompt engineering, AI ethics and explainability, cloud platforms for AI, and MLOps. Employers are prioritizing professionals who can apply AI in real-world settings, not just build models.
Do you need a technical background to learn AI skills?
No, a technical background is not required to start learning AI skills. Many roles focus on applied AI, strategy, ethics, and workflow optimization, which you can learn through short-term courses and hands-on practice.
How long does it take to learn AI skills?
Learning foundational AI skills can take a few months with consistent study, while deeper specialization may take longer. Many professionals build job-ready AI skills through part-time, online certificate programs designed for working adults.
Learn practical skills from expert instructors to boost your resume or succeed in your current role.