AI engineers vs. software engineers: how AI is changing the experience of building software

How software engineering and AI engineering differ (and where they overlap)

Key differences between software engineers and AI engineers:

• System type: Software engineers build deterministic systems with predictable outputs. AI engineers build probabilistic systems that improve through learning and adaptation.
• Primary focus: Software engineers optimize for reliability and maintainability. AI engineers optimize for model accuracy and adaptation.
• Core challenges: Software engineers face slow builds and technical debt. AI engineers face data drift and reproducibility issues.

Software engineering encompasses the design, development, testing, and maintenance of deterministic systems. These systems follow explicit logic: given the same input, they consistently produce the same output. Every developer masters these fundamentals before specializing in a particular area.

AI engineering builds probabilistic systems that learn from data. These systems improve through exposure to examples rather than through explicit programming. An AI model’s behavior changes as it encounters new data, introducing a fundamentally different set of challenges related to reproducibility, bias, and drift.

This doesn’t make AI a separate discipline. It’s a specialization within the software development life cycle, one that extends traditional engineering with statistical methods and continuous retraining. The best teams view these shifts as expanding their toolkit, not replacing their foundation.

Here’s how the day-to-day work differs in practice:

These roles are converging rapidly. Software engineers now embed AI into everyday tools and workflows, a shift we refer to as AI-augmented development. Meanwhile, AI engineers must master traditional software engineering practices to deploy and maintain production systems.

What AI engineers do: building and maintaining learning systems

Four core responsibilities of AI engineers:

1. Model development: Build and train models using frameworks like PyTorch or TensorFlow
2. Validation and testing: Test for accuracy, fairness, and edge case performance
3. Production integration: Deploy models into scalable production systems
4. Monitoring and retraining: Track performance degradation and retrain as needed

AI engineers design, deploy, and monitor learning systems that automate or augment decision-making. Unlike traditional software that executes predefined logic, these systems extract patterns from data and apply them to new situations.

The work breaks down into distinct phases:

Model development: AI engineers build models using frameworks like PyTorch or TensorFlow. This involves selecting architectures, preparing training data, and running experiments to optimize performance. A single model might go through dozens of iterations before it’s production-ready.

Validation and testing: Beyond traditional unit tests, AI engineers validate models against held-out data, test for fairness across demographic groups, and stress-test edge cases to ensure robustness. They must ensure models perform well on data they’ve never seen.

Production integration: Once validated, models must be packaged, versioned, and deployed into production systems. This requires collaboration with platform engineering teams to ensure models can scale, maintain low latency, and integrate with existing services.

Monitoring and retraining: Unlike traditional software, AI models degrade over time as real-world data drifts from training data. AI engineers monitor performance metrics continuously and retrain models when accuracy drops.

This work mirrors the DevOps culture of continuous delivery and shared ownership. AI engineers, like platform engineering teams, prioritize maintainability, documentation, and reproducibility. These are foundational to a strong developer experience.

Why continuous learning matters more for AI engineers

Why AI engineers must learn continuously:

• New architectures and techniques emerge monthly
• State-of-the-art approaches become obsolete within a year
• Cross-functional collaboration requires understanding evolving best practices
• Reproducible experiments depend on mastering new tools and frameworks

The pace of AI research creates a unique pressure. New architectures, training techniques, and optimization methods emerge on a monthly basis. An approach that represents state-of-the-art today may be obsolete within a year.

Successful AI engineers dedicate a significant amount of time to continuous learning. They read recent papers, experiment with new frameworks, and participate in cross-functional discussions about emerging techniques. This isn’t optional professional development. It’s core to the role.

This learning cycle parallels how high-performing teams manage improvement through the use of flow metrics. In DX’s Core 4 model, feedback loops and cognitive load determine productivity. Engineers who reduce friction through the use of better tools, more precise documentation, and automated processes tend to maintain higher throughput and satisfaction.

The same principle applies to AI engineering. Teams that invest in experiment tracking, reproducible environments, and knowledge sharing sustain developer velocity as the field evolves. Those that don’t accumulate technical debt in the form of undocumented models and irreproducible experiments.

How AI model deployment challenges software engineering practices

Three ways AI deployment differs from traditional software:

1. Complex observability: Monitor model accuracy, prediction bias, and behavioral drift, not just errors and latency
2. Extended versioning: Track training code, data versions, hyperparameters, and framework versions for reproducibility
3. Different performance tradeoffs: Balance model accuracy against inference speed and resource consumption

Training a model is only half the work. Deploying it into production introduces engineering challenges that blend traditional software practices with AI-specific concerns.

Observability becomes more complex: Traditional software monitoring tracks errors, latency, and resource usage. AI systems require additional monitoring of model behavior to ensure optimal performance. Is accuracy degrading? Are predictions skewing toward particular outcomes? Are there patterns in failed predictions?

Versioning extends beyond code: A deployed model depends on its training code, the specific version of its training data, hyperparameters, and the framework versions used. Reproducing a model’s behavior requires tracking all these dependencies, not just the code that generated it. This complexity makes technical documentation even more critical for AI systems.

Performance optimization differs: Traditional software optimization focuses on algorithmic efficiency and resource management. AI model optimization also considers inference speed, model size, and the tradeoff between accuracy and latency. A model that’s accurate but too slow for production is useless.

Many teams measure these outcomes through DevOps metrics and engineering KPIs. This deployment phase often reveals hidden friction, which DX refers to as “experience gaps,” that can be quantified through the Developer Experience Index.

How software engineers are adapting to AI-augmented workflows

How AI changes daily software engineering work:

• 92% of developers use AI tools for code generation, refactoring, and review
• Engineers must learn to prompt AI tools effectively and validate outputs rigorously
• Teams pair AI assistance with established practices like test automation and code reviews
• The focus shifts from writing all code to directing AI and ensuring quality

Software engineers remain the foundation of technology organizations. They handle requirements gathering, architecture design, implementation, testing, debugging, and production support. These fundamentals haven’t changed.

What’s changing is how they work. In our study on the impact of AI on developer productivity, 92% of developers reported using AI tools to accelerate tasks like code generation, refactoring, and code review. The result: improved software quality and reduced cycle time for routine tasks.

But AI assistance introduces new considerations. Engineers must learn to effectively prompt AI tools, rigorously validate generated code, and understand when to rely on AI suggestions versus their own judgment. This isn’t passive acceptance of AI output. It’s active collaboration with a new class of tools.

The best engineering teams pair AI assistance with established practices, such as test automation and production readiness checklists. These guardrails ensure that velocity gains from AI don’t come at the cost of reliability or maintainability. Many teams also implement code review checklists specifically designed to catch AI-generated code issues.

How AI analyzes the development process itself

What software engineering intelligence reveals:

• Which types of changes take the longest to deploy
• Which codebase areas generate the most incidents
• What factors predict project delays
• Which teams experience elevated cognitive load

A newer frontier, software engineering intelligence, applies AI to the development process itself. Rather than building AI for customers, teams use AI to understand and improve their own software development processes.

This involves tracking software development metrics and applying machine learning to engineering telemetry. The goal is to identify bottlenecks, predict risks, and optimize the delivery process to ensure seamless execution.

Teams that use value stream analysis or DORA metrics already collect rich data about their development processes. Machine learning can surface patterns in this data that aren’t obvious through manual analysis. Which types of changes take the longest to deploy? Which areas of the codebase generate the most incidents? What factors predict project delays?

For example, predictive analytics can estimate completion times based on historical patterns, flag pull requests that are likely to introduce bugs, or identify teams experiencing an elevated cognitive load. These insights align with practices used by top engineering teams to drive continuous improvement.

Why AI won’t replace software engineers

What AI automates vs. what remains human:

• AI automates: Boilerplate code, documentation updates, routine refactoring
• Humans provide: Creative problem-solving, architectural decisions, cross-team collaboration, and understanding user needs.
• Research shows: Psychological safety and autonomy predict productivity, not automation alone.

The short answer: AI replaces tasks, not judgment.

AI will continue to automate repetitive tasks such as boilerplate code generation, documentation updates, and routine refactoring. These are valuable time savings. But the creative work of engineering remains human: understanding user needs, making architectural tradeoffs, debugging complex systems, and collaborating across teams. This distinction matters when measuring developer productivity.

DX’s research on developer satisfaction and performance reinforces this. Psychological safety and autonomy, not automation, predict sustainable productivity. Teams that measure these factors through SPACE metrics understand that developer well-being has a direct impact on performance.

The engineers who thrive will be those who utilize AI to automate mundane tasks and concentrate their time on high-value problems. Those who resist AI tools will find themselves spending time on tasks that could be automated, while their peers tackle more challenging work.

Why the AI vs. software engineering debate misses the point

The question isn’t whether AI engineering and software engineering are different roles. They are. The question is whether this difference has practical significance.

It doesn’t. AI and software engineering are converging. The engineers succeeding today are fluent in both deterministic and probabilistic systems, which DX refers to as AI-augmented developers. They understand when to build rule-based logic and when to train a model. They know how to validate both traditional code and ML outputs.

Leaders who measure engineering efficiency through frameworks like DevOps KPIs see this clearly. AI amplifies human capability when integrated thoughtfully. It creates friction when treated as a separate discipline disconnected from core engineering practices.

The real challenge isn’t choosing between AI engineers and software engineers. It’s about building teams where both skill sets complement each other, where traditional engineering rigor enhances AI systems, and where AI capabilities extend the capabilities of traditional software.

What engineering leaders need to know about AI, ML, and DL

Engineering leaders face a practical problem: AI, machine learning, and deep learning are used interchangeably in hiring, planning, and strategy discussions. But they represent different levels of complexity and require different investments.

Understanding these distinctions helps leaders make informed decisions about which skills to hire for, which problems to tackle with AI, and where to focus training efforts.

Each layer builds on the previous one. All deep learning is a type of machine learning, and all machine learning is a subset of AI, but the reverse isn’t true. A rule-based expert system is an example of AI, but not machine learning. A decision tree is a type of machine learning, but not a form of deep learning.

When to use each approach:

• Traditional ML: Best for structured data problems, fraud detection, forecasting, and recommendation systems
• Deep Learning: Superior for unstructured data like images, text, speech, but requires more data and compute
• Rule-based AI: Appropriate for well-defined logic and compliance-driven systems

For most organizations, traditional machine learning solves the majority of problems. Deep learning delivers superior results on unstructured data, such as images and text, but requires more data, more compute, and more specialized expertise. Leaders should tailor their approach to the specific problem, rather than adopting the most advanced technique by default.

What the future of engineering looks like

AI isn’t replacing software engineers. It’s changing what engineering means. The future belongs to teams that integrate AI thoughtfully, measure its impact rigorously, and maintain the engineering fundamentals that ensure reliability at scale.

The best teams will use developer experience frameworks, AI ROI models, and tools like AI code analysis to track whether AI tools actually improve outcomes. They’ll ask hard questions: Is AI reducing cognitive load or adding it? Are we shipping faster without sacrificing quality? Are developers more satisfied or more frustrated?

Organizations serious about measuring these impacts can explore collaborative AI coding practices and evaluate specific tools through frameworks like the AI coding tools ROI calculator.

Because the future of engineering belongs to those who don’t just build more intelligent systems, but also build more innovative experiences for the people who use those systems.