The tech market in LATAM is going through one of its moments of greatest demand for roles related to artificial intelligence. Two profiles lead the searches: the AI Engineer and the ML Engineer. They are different, use different stacks, and point to careers with their own trajectories. If you're thinking about which of the two to follow, this article gives you the information you need to decide.

The explosion of LLMs in production created confusion in the market: many companies use the terms interchangeably, but there are concrete differences that impact both the day-to-day work and the job opportunities and salaries. Understanding those differences today is key to positioning yourself well in the tech market of Argentina and the region.

What does an AI Engineer do?

The AI Engineer works with already-trained language models (LLMs) to build applications and products that use them. Their focus is on integration, prompt design, data pipelines, and the deployment of AI solutions in production.

They don't train models from scratch: they take existing models like GPT, Claude, or Gemini and connect them with databases, APIs, and real workflows. It's the profile that makes AI useful within a company or product.

Typical AI Engineer stack:

• LLM frameworks: LangChain, LlamaIndex, LangGraph

• Vector databases: Pinecone, Weaviate, Chroma

• Model APIs: OpenAI, Anthropic, Google Vertex AI

• Infrastructure: FastAPI, Docker, AWS/GCP, Redis

• Observability: LangSmith, Weave, Datadog

What does an ML Engineer do?

The ML Engineer designs, trains, optimizes, and puts machine learning models into production. They work closer to statistical mathematics and raw data. Their task is to build models that learn from data and make decisions about architectures, hyperparameters, and training pipelines.

It's the profile that historically dominated AI before the LLM era: classification, prediction, computer vision, and natural language processing models with classic techniques and neural networks.

Typical ML Engineer stack:

• Frameworks: PyTorch, TensorFlow, Scikit-learn, JAX

• MLOps: MLflow, Weights & Biases, DVC, Kubeflow

• Data: Pandas, Spark, dbt, BigQuery

• Infrastructure: Kubernetes, SageMaker, Vertex AI

• Monitoring: Evidently, Arize, WhyLabs

Key differences between AI Engineer and ML Engineer

Salaries in Argentina and LATAM

Both roles are among the best paid in the region's tech ecosystem. According to data from portals like Glassdoor, LinkedIn Jobs, and Argentine tech community surveys, the current salary ranges are:

AI Engineer in Argentina:

• Junior: USD 1,500 – 2,500 monthly

• Semi senior: USD 2,500 – 4,000 monthly

• Senior: USD 4,000 – 7,000+ monthly

ML Engineer in Argentina:

• Junior: USD 1,800 – 3,000 monthly

• Semi senior: USD 3,000 – 5,000 monthly

• Senior: USD 5,000 – 9,000+ monthly

The senior ML Engineer tends to earn more in research roles and large companies. The AI Engineer, on the other hand, has a shorter entry ramp and very broad demand in the digital products market. According to McKinsey, generative AI could add between USD 2.6 and 4.4 trillion annually to the global economy, which explains why the demand for profiles that integrate this technology keeps growing.

Which path to bet on based on your profile?

The decision depends more on your starting point than on which role "pays more".

Choose to be an AI Engineer if: you have a base in programming (Python, APIs, backend), you're interested in building products fast, or you come from a business or product profile and want to get into AI. The learning curve is more accessible and the demand is extremely high right now.

Choose to be an ML Engineer if: you have a mathematical and statistical base, you're passionate about research, fine-tuning models, or you want to work at top-tier AI companies or in research labs. It's a longer path but with very high valuation in the senior market.

As an additional reference, the World Economic Forum projects that AI and machine learning roles will be the fastest-growing globally in the coming years, with demand that exceeds the supply of available talent in the region.

If you're exploring the broader world of AI, you might also be interested in reading about what the Chief AI Officer is and why 76% of companies have already created that role, a position that works directly with AI Engineers and ML Engineers.

Coderhouse courses to start your career in AI

If you want to take the first step toward these roles, Coderhouse has options for different levels:

• Introduction to Artificial Intelligence: ideal if you're starting out and want to understand the fundamentals before specializing.

• AI Engineering Course: to build applications with LLMs, RAG, agents, and deployment in production. Focused directly on the AI Engineer profile.

• AI Agents Course: if you already have a base and want to work with autonomous agents, multi-agent workflows, and AI systems that make decisions.

Frequently asked questions

Can an AI Engineer become an ML Engineer?

Yes, but it requires an additional investment in mathematics and statistics. The reverse path (ML → AI Engineer) is usually more direct, because someone who knows how to train models can also integrate them. The important thing is to start where you can enter fastest and keep learning.

How long does it take to train as an AI Engineer from scratch?

It depends on your previous base. If you have programming knowledge in Python, between 6 and 12 months of intensive training is a realistic range to be ready for junior positions. Without a technical base, the process can take between 12 and 18 months.

Do you need to know advanced mathematics to be an AI Engineer?

Not at the level required for an ML Engineer. The AI Engineer works mainly with high-level APIs and frameworks. A basic understanding of probability and statistics is useful, but it's not the center of the daily work. The ML Engineer does need to master linear algebra, calculus, and advanced statistics.

Which has more job demand in Argentina today?

The AI Engineer has greater relative demand in the current market, especially in startups and product companies that want to integrate LLMs quickly. The ML Engineer has more demand in large companies, financial corporations, and companies with large volumes of proprietary data. Both have excellent job prospects.

Can I work remotely for international companies in these roles?

Yes, it's one of the most attractive aspects of both profiles. Most global tech companies hire AI Engineers and ML Engineers remotely from Argentina and LATAM. Platforms like LinkedIn, Toptal, Turing, and Remote remain active channels for finding these opportunities.