AI isn’t a future idea anymore—it’s here, changing industries, automating tasks, and requiring new skills. So, what does this mean for Engineering?
According to research by the Oak Ridge National Laboratory, AI could write most of its code by 2040. This doesn’t mean that developers are on their way out. Instead, AI will boost their productivity by enhancing repetitive tasks. This will free up developers to tackle the big, strategic challenges.
The field of artificial intelligence (AI) and machine learning (ML) is rapidly expanding, yet there’s a notable talent shortage. There are approximately 150,000 machine learning engineers worldwide, a small fraction compared to the 29 million software engineers globally.
Good news? Many software engineers have transferable skills that, with targeted training, can facilitate a transition into AI/ML roles.
Let’s be honest, you don’t always need a specialized AI/ML Engineer. Consider which skills can easily transfer to AI/ML roles and where training or on-the-job practice can bridge the gap.
Core tech skills remain in high demand
A recent Q4 study by BairesDev highlights that core tech skills remain in high demand. Key languages such as React, .NET, Python, Node.js, and Java are essential for both traditional development and AI projects.
Demand for Machine learning is projected to grow by 383% by the end of 2025, which increases demand for Angular, Flutter, Kotlin, and Terraform.
(New) AI skills for developers
As AI becomes more common, there’s a growing need for strong data infrastructure skills. Skills in operating Databricks, Snowflake, and MongoDB are in high demand because these platforms are essential for managing and processing the massive amounts of data that power AI systems.
Meanwhile, demand for machine learning operations (MLOps) is growing. With only 1 in 10 engineers having MLOps expertise.
Another rising trend is retrieval-augmented generation (RAG), which allows AI models to pull in real-time information to improve their answers. Similarly, creating knowledge graphs helps organize messy data into clear connections, making it easier to improve search results, recommendations, and decisions.
Tools like PyTorch and TensorFlow are still key for building and fine-tuning AI models. Demand for them continues to grow.
How employers are approaching the AI revolution
In the DACH region, companies are taking smart steps to close the AI talent gap. Angelina Ebeling, Founder and Managing Director at acework, puts it this way:
“We see a clear pattern with DACH-based employers — whether it’s software houses, SMBs, or Mittelstand businesses. They’re struggling to find AI talent locally, so they’re doubling down on reskilling their existing teams. It’s a win-win: employees stay relevant, and companies stay competitive.”
Here are a few examples of how your current technical talent pool can evolve to meet AI skills demands.
Software Dev ➡️ ML/AI Engineer
Core Skills: Programming languages (Python, Java, C++), algorithms, debugging, and problem-solving.
AI Pathways: Machine Learning Engineer, AI Developer.
Action Plan:
Master tools like TensorFlow or PyTorch.
Build AI Projects e.g., develop chatbots, recommendation engines, or image recognition systems.
Consider certifications like the Google TensorFlow Developer.
Data Analysis ➡️ Data Science
Core Skills: SQL, statistical analysis, data visualization, and reporting.
AI Pathways: Data Scientist, AI Analyst.
Action Plan:
Expand Your Toolkit: Learn Python libraries like pandas and NumPy.
Master ML techniques like regression, clustering, and classification.
Build Predictive Models: Work on projects that forecast trends or model customer behavior.
DevOps ➡️ AI Infrastructure
Core Skills: Automation, CI/CD pipelines, and cloud platforms (AWS, Azure).
AI Pathways: MLOps Engineer, AI Infrastructure Specialist.
Action Plan:
Get familiar with Kubeflow and MLflow for managing machine learning lifecycles.
Deploy and manage AI models on cloud platforms like AWS SageMaker or Azure ML.
Looking to grow your career or build your AI-ready team? acework connects tech professionals and hiring managers with proven employers. We work with startups and established mid-sized companies that offer stability, flexibility, and sometimes remote working options.
