Building a career as a Tech Lead in AI is rarely a straight line. For most people, it’s a mix of curiosity, real ownership opportunities, and constant learning, often in unexpected directions.

In my case, the path started at Universidad de la República (UdelaR) in Montevideo, moved through a shift into industry, and eventually led me to becoming a Tech Lead at Marvik. Not because “one day I got promoted,” but because the work demanded it, and the environment made it possible.

I’m María Noel Espinosa, Tech Lead at Marvik. This is my journey.

From Engineering School to Building Real Systems

I studied Electrical Engineering, a program with a very broad foundation. You touch advanced math, physics, electronics, telecommunications, and you can also explore machine learning and data science along the way. I started without a clear idea of what I wanted to do, and over time I learned what I actually enjoyed (and what I didn’t).

Looking back, the biggest thing I took from university wasn’t a specific tool or framework. It was learning how to learn quickly, under pressure, across very different topics. That discipline and problem-solving mindset translates directly into real-world engineering work.

And there’s one more thing that has mattered more than I expected: English. In AI and software, most documentation, papers, and tooling are in English. It’s one of those skills you invest in early and only fully appreciate later.

Why I Joined Marvik: Curiosity, Engineering, and Variety

I joined Marvik out of curiosity. I wasn’t happy with what I was doing before, it was a different field, and I wanted work that felt like real engineering.

A friend who worked at Marvik told me something that stuck: applying AI in industry is completely different from studying it in theory. In university, the area hadn’t fully clicked for me. But the promise here was different: complex problems, diverse projects, and visible impact.

That was the first turning point: I realized I wasn’t just looking to “work in AI.” I wanted to build systems, make decisions, and solve meaningful problems end to end.

The Turning Point: When the Project Forces Ownership

In consulting, projects vary a lot in size and shape. In my case, I landed in a smaller project with both AI and software components. On the AI side, the team was small enough that many things were simply “there” waiting for someone to take them.

At first, I had support. But that environment creates something powerful: if you want to grow, there’s space to step up.

That’s when I started gaining seniority fast. Part of it was technical growth, but part of it was personal: I’m very detail-oriented, and in AI + production systems, details matter more than people think. In the right context, that becomes a real advantage.

My First Leadership Experience Didn’t Start as a Title

My first real leadership moment didn’t come from a formal “Tech Lead” label. It came from seeing a gap.

On another project, I noticed the machine learning work didn’t have clear direction. The person responsible was blocked, the team was under pressure, and I could see the risk of the project going off track.

I didn’t experience it as “now I’m leading.” I experienced it like engineering: there’s a problem, and it needs to be solved.

I spoke with the project lead, made sure roles and sensitivities were respected, and started supporting where it was needed: asking the questions I knew a senior lead would ask, helping shape technical decisions, and pushing clarity on next steps. I also aligned continuously with leadership, so it stayed healthy and constructive.

That’s how leadership began for me: as service to the team and the project, not as a title.

What Being a Tech Lead in AI Actually Means Today

One of the biggest lessons I learned is that being a Tech Lead isn’t the same as being the strongest individual contributor. It’s a different job.

Tech Lead vs Management

As a Tech Lead, you still need technical depth. But your impact isn’t about doing everything yourself, it’s about enabling decisions, aligning the team, and anticipating what’s coming.

The Hard Part: People, Context, and Anticipation

The hardest part isn’t the model or the stack. It’s people.

• Understanding how different people process feedback
• Detecting friction before it becomes a crisis
• Aligning expectations across client, delivery, and engineering
• Thinking ahead: what the team will need, where risk is hiding, what will break at scale

Leadership has a strong human component, and it requires a different set of skills than pure development.

The Best Part: Helping Others Grow

At the same time, the human side is the most rewarding.

One thing that surprised me is how much I enjoy mentoring and growth follow-ups, supporting someone who’s stepping into a bigger role, helping them build confidence, and watching them succeed.

At some point, the role shifts from “how do I shine?” to “how do I help the team shine?”

Why Marvik: Technical Excellence + Human Leadership

There are two things I always highlight about Marvik:

1. Real technical excellence. The team is genuinely strong. You learn constantly, and the work often pushes you into new ground. “State of the art” can sound like marketing, but here it often means solving problems that are actually new in practice.
2. A human culture. Marvik has grown a lot, but it still feels human. Leadership genuinely cares about people doing well, not just output. And the environment allows you to experiment, take ownership, and grow gradually, instead of forcing you into rigid boxes.

In my experience, growth is always a mix of opportunity and personal drive. Marvik has consistently provided the opportunity side of that equation.

The Future of Tech Leads in AI

The Tech Lead role in AI is becoming more central for one reason: the hard part today isn’t building a demo, it’s running real systems reliably.

Tech Leads increasingly need to bring together:

• Scalable architectures (data, MLOps, inference, cost control)
• Reliability (observability, evaluation, testing, security)
• Human oversight and adoption (governance, workflows, trust)
• And most importantly: judgment, what to build, what not to build, and why

The future isn’t just “more AI.” It’s AI, strong engineering and teams that operate well together.

If You Want to Grow at Marvik, Here’s What Helps

If you’re considering Marvik, these traits will accelerate your growth:

• Genuine curiosity (you like learning and asking questions)
• Ownership (if you see a problem, you move toward it)
• Clear communication (technical and human)
• Comfort stepping into ambiguity
• A team mindset (your goal is collective progress, not solo performance)

Marvik has room for junior engineers who want to grow fast, and for senior profiles who want to lead or deepen expertise. The key is being willing to take opportunities when they appear.

Join Us

If you want to work on challenging AI systems, with high technical standards and a strong human culture, Marvik can be a great place to grow.

We’re hiring and expanding our team. If you’d like to connect, reach out, or explore our open roles and start the conversation.