Focus on the right skills to become an AI Engineer. Today’s newsletter breaks down what to focus on as an AI Engineer and what to look for in an AI Engineer.

I am starting a new live cohort of AI Engineer HQ BootCamp on 23rd July 2025, 7 PM IST. Register Here.

In today’s edition:

• AI Roundup— Grok 4 is here.

• Dive Deep Drill— AI Engineer in 2025: Expectations vs. Reality

• Build Together— Here’s How I Can Help You

AI Roundup

Sponsor Spotlight

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Dive Deep Drill

AI Engineer in 2025: Expectations vs. Reality

If you’re learning AI engineering in 2025 or leading teams building AI solutions, it’s time to pause and ask:

Are we focusing on the right things?

There’s a growing gap between what companies need from AI engineers and what most aspiring engineers think they should learn.

Here’s what I’ve seen while training professionals in my AI Engineer HQ bootcamp and working with AI teams through consulting.

Let’s dive deep.

What Most Learners Think They Need

When I talk to people learning AI or reviewing popular learning roadmaps,

I often see:

• too much focus on the theory of linear algebra, calculus, and every ML algorithm

• not enough focus on deployment and skipping MLOps, CI/CD, or working with APIs

• over-focus on research and trying to invent new models instead of building real-world solutions

• aim for PhDs, they think they need a PhD to break in, but real-world project experience matters more

In short,

people think AI engineering is research.

But it’s software engineering + machine learning(or DL or GenAI) + business value.

5 Skills Companies Want from AI Engineers

Companies today aren’t just experimenting with AI.

They’re building real products that go live, solve business problems, and serve users.

So what skills are they hiring for?

1) End-to-End Engineering

You have been building models, well, that’s just the start.

Companies want engineers who can deploy models, monitor them, retrain them, and make them work in the real world.

This involves understanding MLOps tools and how to maintain model health in production.

To be more specific with MLOps:

• containerization with Docker

• CI/CD for Machine Learning (GitHub Actions)

• ml Workflow Orchestration (Airflow, Kubeflow, or Vertex AI Pipelines)

• monitoring and Observability

• infrastructure as code (Terraform)

2) Cloud Experience

Most AI systems run on cloud platforms like AWS, Azure, or GCP.

Knowing services like SageMaker, Vertex AI, or Azure ML is no longer a bonus, it’s expected.

While the hands-on work is mostly API calls or local deployment.

To meet employer expectations, you must be comfortable within a major cloud ecosystem:

• managed ML services (AWS SageMaker, Azure ML, Google Vertex AI)

• cloud-native data storage and processing (AWS RDS, DynamoDB)

• serverless deployment (AWS Lambda or Azure Functions)

Even if I wrote about how AI Certifications are a Waste of Time,

I also added why the only type of certifications with value in the market is cloud certifications.

3) Strong Programming and Data Handling

Python is still the language of choice.

But companies also want engineers who can work with SQL, big data tools (like Spark), and who understand data engineering.

If you are a beginner, you should start with Python [YouTube]

Dropping an AI Engineering Beginner’s Guide on YouTube, stay tuned.

4) Real-World ML

It’s not about knowing 100 algorithms.

It’s about applying the right one, solving the right problem, and shipping something useful.

5) Ethical and Responsible AI

Engineers are being asked to think about bias, fairness, and transparency.

This is now a part of the job, not a side note.

The AI Engineer Role Is Changing

Here’s what’s true in 2025:

• AI engineering is not data science, it’s more focused on building and deploying systems

• it’s not AI research either, you’re not inventing the next GPT, you’re making sure the current one works in production

• it’s deeply applied, you’re solving real problems, with real deadlines, for real customers

What You Should Focus On

If you’re preparing for or hiring AI engineers today, focus on:

• Python, Git, APIs, Cloud platforms, and MLOps

• Data engineering concepts and working with large datasets

• Model evaluation and making sure things work at scale

• Ethical AI for bias and explaining decisions

Final Thought

The AI engineer in 2025 is part ML expert, part software builder, and part systems thinker.

They don’t just train models, they own the full pipeline, from data to deployment.

If you’re learning, shift your focus from theory to practice.

If you’re leading, hire people who can ship, not just experiment.

This is the reality of AI engineering today.

And this is exactly what I teach inside AI Engineer HQ for engineers, to ship real projects.

Bridge the gap between learning and real-world impact.

One deployed model at a time.

PS: Join AI Engineer HQ July Cohort Now, early bird access 7 left, 13 out of 20 are taken.

Want to work together? Here’s How I Can Help You

• AI Engineering & Consulting (B2B) at Dextar—[Request a Brainstorm]

• You are a leader?—Join [The Elite]

• Become an AI Engineer in 2025—[AI Engineer HQ]

• AI Training for Enterprise Team—[MasterDexter]

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Paper Unfold

A series in which you will get a breakdown of complex research papers into easy-to-understand pointers. If you missed the previous ones:

• Apple's Bomb on Reasoning Models

• Agents are Not Enough [Paper Unfold]

• Attention Is All You Need [Paper Unfold]

AI Engineer HQ [Starting 23rd July, 7 PM IST]

New Cohort starting on 23rd July, 7 PM IST, reply to this email for details.

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