Leading the engineering discipline at Able, I've had the privilege of working with a team that is both exceptional and naturally curious. Experimentation is in our DNA—we’ve always been early adopters, thoughtfully evaluating new tools and technologies before integrating them into our stack.
When GenAI tools started gaining traction in late 2022, we approached them with both enthusiasm and healthy skepticism. Through structured experiments, we benchmarked their impact on code quality, development velocity, and overall workflow efficiency. It quickly became clear that this wasn’t just another incremental improvement—it was a fundamental shift in how software is built.
As engineers integrate AI into their daily work, we’ve seen a natural expansion beyond traditional role boundaries. Some wrestle with the transition, adjusting to a reality where their expertise is less about writing every line and more about guiding AI effectively. Others see it as a liberation from repetitive tasks, allowing them to focus on deeper problem-solving.
What’s clear is that the role of engineers is evolving. The most valuable skills are shifting—from syntax mastery to systems thinking, from code production to AI collaboration, and from pure implementation to defining and framing complex problems.
Our engineers have shared how much the developer experience has improved, likening it to the early days of Ruby on Rails—freeing them from tedious boilerplate and allowing them to focus on creativity and problem-solving.
Engineers no longer need to obsess over specific language details, allowing them to move fluidly between JavaScript, Python, or Rails as the problem demands. This flexibility has accelerated our ability to select the right tool for each job.
Our teams are building experimental projects at an unprecedented rate. Ideas that would have been dismissed as ‘too much effort for the potential return’ are now regularly prototyped in days rather than weeks.
The ability to quickly generate code, test an approach, discard it if necessary, and rebuild has transformed how we approach problem-solving. This disposability of code—being able to try multiple approaches without significant investment—has led to more creative solutions.
With AI streamlining certain implementation details and automating repetitive tasks, our engineers can focus more on system design, user experience, and making thoughtful architectural decisions.
There's an important distinction emerging between teams that develop AI systems and teams that use AI as part of their development process. The former requires specialized AI engineering talent, while the latter represents the new normal for all engineering teams.
The traditional "two-pizza" team model remains effective at Able, but the composition of these teams is evolving rapidly.
We're also seeing AI melt away rigid boundaries that traditionally existed in companies. Developers can now operate as designers and product managers, while product managers can contribute to coding tasks. This fluidity creates both opportunities and challenges for team composition and collaboration.
The role of software engineers is rapidly evolving as AI becomes deeply integrated into the dev cycle. Let's map out how these engineering roles are evolving:
The modern full stack engineer integrates AI capabilities throughout the entire application stack. They now implement AI features across front-end experiences, API layers, and back-end systems. They leverage AI to rapidly prototype complete applications and spend more time on architecture decisions while AI handles repetitive tasks. These engineers focus on connecting AI services into cohesive applications rather than building the AI models themselves.
Front-end engineers now incorporate AI-driven components, intelligent user interfaces, and predictive UX patterns. They leverage models for UI development, automate accessibility testing, and implement personalized user experiences powered by real-time AI adaptation. Their coding workflow is accelerated allowing them to focus on creative design challenges and user-centered innovations.
Back-end engineers now design and implement systems that seamlessly integrate with AI services and ML models. They build APIs optimized for AI interaction, design data flow architectures that support ML pipelines, and create serverless functions that trigger contextual AI processing. Their expertise extends to performance optimization for AI workloads and designing fault-tolerant systems for AI components.
Data engineers have evolved to specialize in building the data foundations that power machine learning systems. They design data pipelines optimized for ML training, implement feature stores, and ensure data quality for AI consumption. Their role now includes data versioning, automated data validation, and maintaining the data infrastructure that enables continuous model training. Their focus has shifted from basic ETL processes to creating robust data ecosystems that can reliably fuel AI systems.
DevOps engineers have expanded their toolkit to address the unique challenges of deploying and monitoring machine learning systems. They implement continuous integration for ML models, build automated testing for AI components, and create monitoring systems that detect model drift. Their role now encompasses model deployment, version control for datasets, and maintaining the infrastructure for distributed ML training. The complexity of maintaining AI systems in production has transformed this role from infrastructure management to AI operational excellence.
AI Engineers are evolving into Applied AI Specialists who focus on building and fine-tuning deep learning models for specific business domains. These specialists develop custom neural network architectures, design training pipelines, and optimize model performance for targeted use cases. Their work involves selecting appropriate algorithms, engineering features, tuning hyperparameters, and addressing domain-specific challenges like data imbalance or transfer learning requirements.
They excel at adapting foundation models through techniques like RAG and fine-tuning to solve vertical-specific problems, and they implement complex evaluation frameworks to measure model performance against business KPIs. These engineers apply cutting-edge AI techniques to deliver measurable business outcomes in production environments.
In this age of AI, every engineer will be an AI-powered engineer—and as leaders, our challenge is to foster a culture that embraces this partnership while ensuring we're building not just faster, but a lot better.
The question for engineering leaders isn't whether to adopt AI, but how intentionally you'll shape its integration into your team's workflow. Start by identifying a single role transition that could deliver immediate impact in your organization. Is it empowering your DevOps engineers to build MLOps capabilities? Or perhaps enabling your front-end team with AI-powered design tools? Begin with targeted experiments, measure the outcomes, and let the results guide your broader AI strategy.
The most successful engineering organizations won't be those with the most advanced AI tools, but those who thoughtfully reimagine how their engineers work with these new capabilities.
How will you reshape your engineering team for the AI-powered future?