9 AI development skills tech companies want

Artificial intelligence (AI) is becoming increasingly important in software development, as organizations look to automate tasks, complete projects faster, enhance code quality, and increase developer productivity. AI tools can help with tasks such as detecting bugs, testing software, and generating code.

As artificial intelligence takes on a more vital role in software development, many developers are asking what skills and attributes will help them land their next dream job. We asked tech leaders what they consider the must-have skills for AI-driven development right now.

Here are the 9 AI development skills tech companies want

Ability to assess AI’s impact on the business

Experience with data infrastructure, management, and analytics

Ability to integrate AI tools into existing systems

Experience ensuring AI safety and reliability

Experience with cloud AI deployment

Advanced prompt engineering and LLM integration

Strategic mindset

Excellent time management

Comfort with ambiguity

Ability to assess AI’s impact on the business

AI shops want developers who not only understand the conceptual side of artificial intelligence, machine learning, and deep learning, but also how to apply them to achieve business objectives.

“It’s not enough to know how a transformer model works; what matters is knowing when and why to use AI to drive business outcomes,” says Scott Weller, CTO of AI-powered credit risk analysis platform EnFi. “Developers need to understand the tradeoffs between heuristics, traditional software, and machine learning, as well as how to embed AI in workflows in ways that are practical, measurable, and responsible.”

SleekFlow, provider of an omnichannel customer engagement platform, leans heavily on AI for its products. “We don’t apply AI just for the novelty,” says Lei Gao, CTO at SleekFlow. “Our vision is clean business returns. Developers or engineers must have a grasp of how models such as LLMs or recommender systems are translated into actual value.”

For example, developers need to understand how AI-powered software can help increase conversion rates or customer support automation. “It is not merely creating good models, but utilizing them beneficially in business processes,” Gao says.

As AI takes on more of the low-level coding burden, “developers must increasingly focus on why they’re building something—not just how,” says Mitchell Johnson, chief product development officer at Sonatype, a provider of software supply-chain management tools.

“Understanding the customer domain, product-market fit, and business impact becomes critical,” Johnson says. “AI-native companies value developers who are closer to product management—able to spot user problems, make tradeoffs, and shape what gets built.”

Keep up with AI tutorials and news on InfoWorld: Read the monthly GenAI Report.

Experience with data infrastructure, management, and analytics

AI and machine learning rely on massive volumes of data to be most effective. Therefore, developers need to have a good grasp of data infrastructure, management, and analytics.

“In AI-first systems, data is the product,” Weller says. “Developers must be comfortable acquiring, cleaning, labeling, and analyzing data, because poor data hygiene leads to poor model performance.” This includes familiarity with modern data stacks, SQL, and cloud-native data tools, he says.

AI models are only as strong as the data pipes that power them, Gao says. “We seek engineers with the ability to work with distributed data platforms and orchestrate everything from ingestion to real-time analytics,” he says.

Knowledge of the newer concepts around data mesh, stream processing, and event-driven architecture is becoming a growing requirement, Gao says. “At SleekFlow, we built a distributed messaging infrastructure for high data rates between services, and enabled the ability to run AI models against novel, context-derived inputs,” he says.

“AI applications are only as good as their data, but traditional data engineering approaches fall short for AI workloads,” adds Vaibhav Tupe, technology leader at IT services provider Equinix and IEEE senior member.

“Developers need specialized skills in building data pipelines, creating features specifically optimized for machine learning, and managing data quality tailored to AI needs,” Tupe says. “This involves setting up real-time feature stores, automating data validation, and effectively managing differences between training and inference data.”

Ability to integrate AI tools into existing systems

If AI-powered tools don’t work well with existing systems, customers might not see any benefits.

“As a company that helps industrial firms adopt AI, we take prioritizing developers with strong AI/ML integration and implementation skills very seriously,” says Kevin Miller, CTO at IFS, a provider of industrial software for businesses that manufacture, service, and manage complex assets.

“We know that AI-enabled predictive maintenance is crucial to every one of our customers, so how do we translate that into a well-functioning product?” Miller says. “We need developers who can put two and two together, implement predictive maintenance algorithms that work with industrial tools like SCADA [supervisory control and data acquisition] systems, and create robust data pipelines feeding real-time sensor data to machine learning models.”

Experience ensuring AI safety and reliability

In some sectors, such as industrial manufacturing, AI systems must prioritize safety and reliability. AI-driven companies in these sectors are looking for software engineers with the skills and experience to ensure these qualities.

AI safety and reliability engineering “looks at the zero-tolerance safety environment of factory operations, where AI failures could cause safety incidents or production shutdowns,” Miller says.

To ensure the trust of its customers, IFS needs developers who can build comprehensive monitoring systems to detect when AI predictions become unreliable and implement automated rollback mechanisms to traditional control methods when needed, Miller says.

“This includes developing redundancy systems and extensive testing frameworks that validate AI behavior under edge cases and adversarial conditions,” Miller says.

Experience with cloud-based AI deployments

Given the prominent role of cloud services in today’s IT infrastructures, developers are expected to be experienced with cloud AI deployment and application programming interface (API) integration.

“Today, cloud is everything,” says Naga Santhosh Reddy Vootukuri, principal software engineering manager at Microsoft. As such, developers need to be familiar with using AI tools in cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

“These skills will help them in hosting and also integrating AI solutions with existing legacy systems,” Vootukuri says, by making use of Model Context Protocol (MCP), an open standard that enables AI applications to easily and securely connect to external tools and data sources.

Advanced prompt engineering and LLM Integration

Prompt engineering, the process of structuring or creating an instruction to produce the best possible output from a generative AI (genAI) model, is on the rise, with use cases emerging in a range of industry sectors. Prompt engineering is used for content generation, problem-solving, and language translation, and helps genAI models respond to many types of queries.

“With the rapid growth of large language models, developers now require a deep understanding of prompt design, effective management of context windows, and seamless integration with LLM APIs—skills that extend well beyond basic ChatGPT interactions,” Tupe says.

“Developers must know how to build sophisticated prompt chains, handle large-scale deployments, manage rate limits, optimize costs, and integrate multiple LLMs seamlessly,” Tupe says. “There is a huge difference between developers who simply write basic prompts and those who can design robust, enterprise-ready LLM systems backed by thorough testing, evaluation, and monitoring capabilities.”

Strategic mindset

Developers working on AI-focused projects need to be able to think strategically about what they’re working on.

“As AI takes away a growing percentage of the low-hanging fruit of development, it becomes more important than ever that a developer has a strategic mindset—the ability to look at a problem, analyze it, and determine the direction to take the solution,” says David Radin, CEO of Confirmed, a company that provides a platform for travel optimization, and creator of the “Time Management in the Age of A.I.” workshop.

“AI often gives code from a good prompt, and having a strategic direction in the prompt helps you guide AI to the type of solution you want,” Radin says. “Then, when AI gives you a less-than-adequate response, your strategic mindset will help you analyze the answer and either prompt your AI to move closer to the direction you need or supplement it yourself.”

Also see: What you absolutely cannot vibe code right now.

Excellent time management

Time management is a skill that applies to just about every type of job function, and software developers in AI-focused organizations are no exception.

“In AI-driven shops, great time management skills is still near the top of the list of requirements,” Radin says.

“Not only does it help the organization and the individual to meet their respective goals, in the age of AI it also shows how important humans are to a development center,” Radin says. This reduces the risk of layoffs or concerns about missing deadlines or goals, he says.

Comfort with ambiguity

Developers who work with AI need to be adaptable and open to learning, because technology is constantly in flux.

“Tools and paradigms are changing monthly,” Johnson says. “The growth mindset today means more than just learning—it means defaulting to AI as the starting point. Great developers now instinctively ask, ‘How would I solve this with AI first?’ They reimagine their approach from the ground up, designing processes, tools, and features with AI at the center, not as an add-on.”

Developers also need to be comfortable with ambiguity and rapid iteration, Weller says. “AI development is inherently probabilistic, outputs may vary, systems drift, and feedback loops emerge,” he says. “Developers need the maturity to debug not just broken code, but broken assumptions. The best developers embrace this ambiguity and build systems that are resilient, testable, and evolve over time.”

Being comfortable with rapid iteration is a must-have skill, Weller says. “The space is moving so fast that every developer needs to have a passion for proving assumptions.”