Description
The conversation around AI engineering is shifting, and Alessandro Romano is at the center of it.
In this episode of Let’s Talk AI, we explore why the future of AI is no longer just about tools. It’s now about people who can think critically, solve problems, and apply frameworks with purpose.
Alessandro breaks down the evolution of roles in data science, the rise of hands-on workshops for real-world learning, and the practical use of frameworks like Crew AI and LangGraph.
He also explains why observability isn’t a buzzword but a necessity for responsible AI development.
If you’ve ever wondered how AI engineers can deliver business value without drowning in hype, this episode offers a grounded, professional perspective.
Top Insights:
A professional perspective in data science requires domain specificity.
Experiences at conferences can lead to valuable networking opportunities.
The role of a data scientist is evolving and often overlaps with AI engineering.
AI engineering is about building solutions, not just maintaining infrastructure.
Workshops can be effective for hands-on learning and engagement.
Choosing the right framework depends on the specific problem being solved.
Observability is crucial for understanding AI systems' decision-making processes.
Problem-solving should be prioritized over tool selection in AI development.
Experimentation with tools is essential for effective AI engineering.
A strong foundation in software engineering enhances problem-solving capabilities.
Connect with Alessandro Romano
Connect with Thomas Bustos
Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/
Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai
Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP
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