Top 10 Companies Hiring AI Engineers in Argentina in 2026

Table of Contents

• Standing in the Subte with a CV
• Mercado Libre
• Globant
• Microsoft Argentina
• Ualá
• Despegar
• Satellogic
• Banco Santander Argentina
• Accenture Argentina
• IBM Argentina
• Techint (Tenaris Digital)
• Choosing Your Line on the AI Subte
• Frequently Asked Questions

Mercado Libre

Think of Mercado Libre as Line D leaving Catedral at rush hour: fast, crowded, and feeding half the city. From its Saavedra campus, MELI runs e-commerce and fintech rails for the region, and AI is baked into almost every part of that system.

What you’ll build and ship

ML teams work on production systems that touch millions of users daily, including:

• Recommender models ranking 200M+ listings across LatAm’s largest marketplace
• Fraud and risk engines for Mercado Pago’s payments and credit products
• Logistics algorithms that route drivers and optimize same-day delivery
• Newer GenAI “copilots” for search, catalog management, and internal analytics

Under the hood, MELI is heavily invested in Google Cloud: Spanner, BigQuery, and streaming pipelines described in detail in Google Cloud’s profile of Mercado Libre’s Spanner-based AI foundation. An internal platform called Fury standardizes how teams train, deploy, and monitor models, supporting tens of thousands of application deploys per day.

Team structure, pay, and fit

Over 18,000 engineers work in decentralized squads mapped to business lines (Marketplace, Fintech, Logistics). ML engineers sit inside these squads, backed by central UX Core and AI platform groups. The culture is deeply experiment-driven: constant A/B tests, aggressive product KPIs, and real on-call responsibility when models misbehave in production.

Compensation reflects that pressure. Junior AI/ML roles typically pay ARS 4,500,000-6,000,000 per month (roughly USD 4,500-6,000 at common parallel rates), with seniors in the ARS 12,000,000-18,000,000+ range, plus meaningful RSUs in MELI stock. An independent analysis of Mercado Libre’s AI strategy notes its dominant marketplace share and data scale as key moats in the region’s tech economy, making it a centerpiece in reviews like Klover’s breakdown of MELI’s AI dominance.

This “line” fits you if you want massive datasets, care obsessively about product metrics, and are ready to learn fast inside high-intensity sprints where shipping beats slideware every time.

Globant

If Mercado Libre is a single packed line, Globant is the whole network of services heading out of the Distrito Tecnológico toward clients in New York, Madrid, and São Paulo. Instead of one product, you join rotating “AI Pods” that plug into global enterprises and rebuild how they work with AI.

What you actually build

Globant’s AI Studio focuses on embedding AI into existing businesses rather than inventing new consumer apps. In a given year you might:

• Design agentic workflows that help dev teams ship faster
• Build GenAI assistants for customer support in banking or telecom
• Prototype personalization engines for a sports league or retailer

The company highlights this approach in its AI Studio overview, where AI agents span the entire software development lifecycle.

Tech stack and pod model

Expect heavy multi-cloud work across AWS, GCP, and Azure, plus proprietary tools like Globant CODA (neuro-symbolic AI) and GeneXus Next for low-code AI. Pods are small, multi-disciplinary squads mixing ML engineers, full-stack devs, UX, and product. According to investor materials, almost 100% of Pods are now “AI-certified”, reflecting how deeply AI is woven into the consulting offering.

Compensation and who thrives here

Mid-level AI engineers in Argentina typically see ARS 7,000,000-10,000,000 per month, with leads at ARS 15,000,000+. A significant slice is often indexed to USD or paid via CCL, aligning with Globant’s positioning as a premium nearshore partner in rankings like MOR Software’s list of global AI consulting firms.

You’ll fit this “line” if you like variety, client interaction, and switching contexts every 6-12 months. From Buenos Aires, Córdoba, or Tandil, your day is mostly spent syncing with teams abroad - perfect if you want a portfolio full of recognizable global brands rather than a single product logo.

Microsoft Argentina

Where Globant and MELI are all about visible features, Microsoft’s Buenos Aires hub is closer to the rails under the Subte: you don’t see them, but every train depends on that infrastructure. Local teams plug into global groups working on the core plumbing behind Azure AI and Microsoft’s model-serving stack.

What you work on day to day

Most roles here sit far from end-user UIs. Engineers contribute to Azure AI services, large-scale experimentation platforms, online advertising systems, and the hardware-software co-design that makes model serving cheaper and faster. The direction is aligned with Microsoft Research’s roadmap on what’s next in AI infrastructure, where disaggregated hardware and specialized accelerators drive the next wave of optimization.

Stack, standards, and global teams

You’ll live in a world of PyTorch, ONNX, and Azure ML, plus internal tools for distributed training, evaluation, and agentic testing of complex AI systems. Expect to profile kernels, reason about latency budgets, and debug failures that only appear at massive scale. The interview loop mirrors other Microsoft hubs: LeetCode-style coding, ML and systems design, and behavioral rounds with peers across Redmond, Europe, and India.

Compensation and work style in Buenos Aires

Senior engineers and applied scientists typically earn around ARS 15,000,000-22,000,000 per month, with principals at ARS 25,000,000+. A large share of total compensation comes as USD-denominated stock grants, consistent with packages shown in current Microsoft AI engineering job listings.

If you live within roughly 40 km of the Buenos Aires office, the norm is about four days a week on site, still aligned with North American time zones for smooth nearshore collaboration. This line fits you if you care more about systems, performance, and reliability than about tweaking button colors - and if you want a globally recognized name on your CV while going deep on infra rather than product features.

Ualá

Ualá is the hoodie-and-mate side of Buenos Aires fintech: a prepaid card in almost every kiosk line, and a data engine behind millions of people getting formal financial services for the first time. Its AI teams sit right where credit risk meets inclusion, deciding who gets a higher limit or faster approval in real time.

AI where credit meets inclusion

Most of the work here is classic applied ML on rich tabular data. Teams build and maintain:

• Credit scoring and credit limit optimization models across Argentina and Mexico
• Automated KYC pipelines for document verification and fraud checks
• Spend-categorization and behavioral models that drive insights and in-app nudges

In rankings like Xcapit’s 2026 overview of Argentina’s AI companies, Ualá is framed as a primary challenger to Brazil’s Nubank in the Spanish-speaking market, with AI as a key differentiator in onboarding and risk.

Stack, delivery, and salary bands

The stack is pragmatic: AWS for infrastructure; Python with Scikit-learn and related libraries for modeling; and MLflow to track experiments and deployed versions. Feature stores, near real-time scoring APIs, and dashboards tie models back to product and risk teams.

Engineers sit in “growth” and “risk” tribes, shipping incremental improvements weekly through A/B tests. Mid-level AI/ML engineers typically earn around ARS 7,500,000-10,500,000 per month, with seniors from ARS 13,000,000+. Offers are often benchmarked against regional fintech heavyweights, reflecting how digital banking is expanding alongside the broader LatAm mobile app ecosystem.

Who this line suits

This is your Subte line if you like structured data more than fancy UIs, care about measurable lifts in default rates and approval times, and want your models to unlock credit for people who were previously invisible to traditional banks. Expect a fast cadence, plenty of metrics, and a Palermo office that still feels like a startup more than a bank.

Despegar

Despegar is what happens when you mix Argentine volatility with global travel chaos. One day you’re pricing flights to Bariloche in high season, the next you’re recalculating demand forecasts after a surprise devaluation. Inside that turbulence, AI is the engine trying to keep margins, inventory, and users in sync across an entire continent.

AI teams here work on production systems that shape the full travel funnel:

• Flight and hotel recommendations tuned to behavior, budget, and route constraints
• Dynamic pricing for flights, hotels, and packages across dozens of markets
• Real-time demand forecasting that feeds inventory and marketing decisions
• NLP-driven customer-service bots to deflect tickets from call centers

The stack is pragmatic: AWS as the backbone, Python and Spark for large-scale data processing, and a homegrown MLOps framework to orchestrate batch training and near real-time inference. Data flows in from airlines, hotel chains, other OTAs, and user behavior - spread across multiple currencies, languages, and tax regimes. Rankings of leading machine-learning providers, such as Clutch’s list of top ML companies in Argentina, routinely place Despegar among the most mature travel-tech players in the region.

Org-wise, there’s a central AI research group experimenting with new models, plus embedded ML engineers inside product squads like Flights, Lodging, and Packages. You’ll constantly wrestle with seasonality (winter vs. summer), big macro shocks, and patchy partner data, which keeps the modeling side interesting and very applied.

Compensation reflects strong but not unicorn-level pressure: mid-level ML engineers typically make around ARS 6,500,000-9,000,000 per month, with seniors in the ARS 11,000,000-15,000,000 range. Community salary reports on forums like r/devsarg’s Despegar thread broadly echo these bands.

This is your line if you enjoy time-series, pricing, and marketplace dynamics - and if you want a mix of research-heavy modeling and the very real constraints of planes, beds, and holidays that can’t be moved by a cron job.

Satellogic

Switching from Mercado Libre to Satellogic is like leaving Line D and boarding a long-distance train out of Constitución: same engineering mindset, totally different landscape. Instead of carts and payments, you work with rivers, ports, crop fields, and entire cities as your raw data.

AI-first earth observation

Satellogic runs an “AI-first” constellation of high-resolution satellites. On-orbit and ground-side models handle:

• Object detection for ships, vehicles, infrastructure, and equipment
• Land-use and crop classification across huge agricultural regions
• Change detection to spot new construction, deforestation, or flood damage

A recent contract worth around USD 30 million showcased how these AI services are packaged for governments and enterprises, as detailed in Satellogic’s own announcement of its AI-first constellation services. You’re not just labeling images - you’re helping monitor borders, pipelines, crops, and climate risks at planetary scale.

Stack, salaries, and regional edge

The stack is a mix of Python and PyTorch for model development, with C++ and edge AI running directly on satellites where bandwidth and power are scarce. On the ground, large-scale pipelines and geospatial libraries turn raw imagery into usable analytics for customers in energy, agriculture, and government.

Mid-level computer-vision/ML engineers typically earn around ARS 8,000,000-11,000,000 per month, with leads above ARS 16,000,000. Many roles are fully USD-denominated, according to Glassdoor’s salary snapshots for Satellogic’s Buenos Aires office, which is unusual even in Argentina’s dollar-linked tech scene.

Work is deeply technical and slower-cycle than a pure SaaS startup, with strong collaboration with CONICET and UBA/FIUBA labs in remote sensing and computer vision, plus teams in Uruguay, the US, and Europe. This is your line if you love computer vision and geospatial data, care about climate and infrastructure, and want your models to move tractors and ships rather than click-through rates.

Banco Santander Argentina

In banking, Santander Argentina is the old infrastructure being upgraded while the trains keep running. Its “Data & AI” division sits on top of decades of transaction history and legacy systems, trying to turn that into real-time intelligence without breaking strict regulations or customer trust.

What the AI teams actually do

Most AI work clusters around core banking levers:

• Credit scoring and limit assignment for retail and SME customers
• Fraud detection and transaction monitoring across cards, transfers, and digital channels
• Hyper-personalized offers in mobile and web, tuned to risk and product fit

Because this is a regulated bank, every model has to be explainable and auditable. You’re not just optimizing AUC; you’re dealing with model risk management, documentation, and validation committees that sign off before anything touches production.

Stack, hybrid cloud, and governance

The stack is in transition. A mix of Azure and AWS underpins new workloads, Python is now the default for modeling, while legacy SAS flows are steadily being rewritten. Snowflake is consolidating data that used to live in scattered on-prem systems. This shift mirrors a broader trend where nearly two-thirds of enterprises now use hybrid cloud specifically to power AI initiatives, according to a Gartner pulse survey summarized by Red Hat.

Salaries, structure, and who this line suits

A centralized Data Office partners with Retail, SME, Risk, and Compliance units. Release cycles are slower than in fintech, but there’s more stability and a deeper dive into regulated AI. Junior data/ML engineers generally earn around ARS 3,500,000-4,500,000 per month, while seniors fall in the ARS 9,000,000-13,000,000 range, competitive within traditional banking but below the most aggressive fintech offers.

This is the right line if you want to understand how AI works under tight regulation, enjoy credit and fraud modeling, and prefer the predictability of a global bank to the swings of startup life. It’s also a solid platform if you eventually want to advise other regulated industries, from energy to insurance, in Argentina’s maturing AI ecosystem described in legal analyses like Lexlink’s overview of the country’s AI industry.

Accenture Argentina

Accenture’s Buenos Aires hub is like the transfer station where every line passes through: an Advanced Technology Center that quietly powers AI projects for banks in New York, miners in Chile, and retailers in Europe. Instead of one product, you jump between industries, building and integrating AI into whatever legacy stack the client already has.

Most teams work on applied, production-facing initiatives for global enterprises, such as:

• Industrial automation and predictive analytics for manufacturing and mining
• Customer-service transformation with GenAI agents and intelligent IVRs
• Responsible AI audits and model-governance frameworks for regulated sectors

Analyses of top AI consultancies, like DesignRush’s ranking of AI firms in Argentina, routinely highlight Accenture as a default choice for large enterprises needing end-to-end AI programs.

The tech stack is whatever the client runs, with a bias toward Azure AI and Google Cloud Vertex AI, plus Python, SQL, and Spark. You’ll often touch Databricks, container platforms like OpenShift, and CI/CD pipelines that must satisfy both Accenture and client security rules. It’s classic consulting: lots of documentation, governance, and integration work alongside modeling.

Compensation tracks responsibility more than pure algorithms. As an Analyst or junior ML engineer you typically earn around ARS 2,500,000-3,500,000 per month, rising to roughly ARS 8,000,000-12,000,000 for Managers and Tech Architects, according to aggregated salary data for Buenos Aires. Because clients are mostly in North America and Europe, your day is aligned to their hours, reflecting the broader nearshore model described in guides to LatAm IT staffing and delivery centers.

This is the right line if you want breadth over depth: complex projects, frequent context switching, and a very structured career ladder. From Parque Patricios or Puerto Madero, your “commute” is mostly into other people’s systems and cultures - perfect if you see AI as a cross-industry toolkit rather than a single-product craft.

IBM Argentina

IBM in Argentina is the line that doesn’t trend on Twitter but quietly keeps half the city moving. From its local offices, IBM teams build and run AI systems that sit deep inside banks, oil companies, telcos, and public institutions, where uptime, compliance, and explainability matter more than flashy interfaces.

Enterprise AI on hybrid cloud

The focus here is Watsonx and hybrid cloud orchestration rather than consumer apps. Local engineers deliver:

• NLP solutions for legal and compliance teams processing contracts and regulations
• Sector-specific AI for energy, including long-running collaborations with players like YPF
• Tooling to manage and monitor models across on-prem systems and multiple clouds

This expansion of AI services in the region mirrors IBM’s broader push to grow its Latin American cloud footprint, detailed in coverage of how IBM is extending AI capabilities at its LatAm cloud region.

Stack, structure, and compensation

The stack blends IBM Cloud and Watsonx with Python for modeling, and Red Hat OpenShift to orchestrate containers across hybrid environments. Many roles live at the intersection of ML, DevOps, and security: you’ll define deployment patterns, audit trails, and access controls for highly sensitive workloads.

Senior engineers in Argentina typically earn around ARS 10,000,000-14,000,000 per month in total compensation, including benefits like strong health coverage and training budgets. Benchmarking from platforms like Levels.fyi’s IBM Argentina salary data shows these bands to be competitive with other global enterprises operating in Buenos Aires.

Day to day, you’ll split time between long-term client implementations and internal R&D, interfacing with global IBM research and delivery teams while working hybrid from Buenos Aires. This line fits you if you want to specialize in enterprise-grade AI, enjoy thinking about governance and reliability, and see your ML skills as part of a broader hybrid-cloud and platform-engineering toolkit.

Techint (Tenaris Digital)

Riding the Techint line is swapping a laptop-only office for safety goggles and steel-toed boots. Through units like Tenaris Digital, the group builds AI that keeps steel mills, pipe plants, and energy operations running from Campana to Texas, where every false positive can stop a production line and every miss can cost millions.

Industrial AI on the factory floor

Most work centers on a few high-impact use cases:

• Anomaly detection in steel production, flagging defects or temperature drifts before they become scrap
• Predictive maintenance on turbines, rolling mills, and heavy equipment using sensor time-series
• Supply chain optimization to move pipes, coils, and inputs across ports and yards worldwide

The stack leans on Azure and Python, with specialized IoT/edge tech pushing models close to the machines. Computer vision monitors safety and product quality on the line; streaming models crunch vibration and pressure data in real time. This kind of cross-enterprise AI partnership is exactly the pattern analysts describe when examining how large industrials team with tech players to drive transformation, as in Durapid’s review of AI alliances for enterprise growth.

Organization, salaries, and who thrives here

A centralized Techint Digital group serves plants worldwide. You’ll split weeks between AMBA offices and sites like Campana or Valentín Alsina, sometimes traveling abroad to mills in North America or Europe. Feedback is physical: fewer unplanned shutdowns, safer shifts, better yield.

Senior ML/AI engineers typically earn around ARS 11,000,000-16,000,000 per month, plus strong corporate benefits and performance bonuses. It’s an excellent fit if you enjoy time-series and sensor data, like talking to process engineers as much as PMs, and want your models to move cranes, valves, and furnaces - not just web metrics, echoing the growing global demand for “AI in industry” profiled by sources like EsparkInfo’s survey of industrial AI developers.

Choosing Your Line on the AI Subte

Back on the platform at Plaza Miserere, the map has done its job: you know all the ways to reach Parque Patricios. What it can’t answer is whether you’d rather squeeze into Line D’s chaos, ride a half-empty outer branch, or switch three times to avoid the worst crowds. Argentina’s AI scene is the same: after looking at these ten “lines,” the question stops being “Which is #1?” and becomes “Which trade-offs match where I actually want to end up?”

Each cluster of companies offers a different route:

• E-commerce & fintech (Mercado Libre, Ualá, Despegar, Santander): high experimentation, intense metrics, huge user bases, strong upside but heavy on-call and KPI pressure.
• Enterprise & consulting (Globant, Accenture, IBM, Microsoft): nearshore projects with US/EU clients, broad industry exposure, more process and governance, often with a mix of ARS and USD-indexed pay.
• Deep-tech & industrial (Satellogic, Techint): smaller, specialized teams, longer cycles, and models that move ships, machinery, and infrastructure rather than clicks.

When offers land in your inbox, look past the brand. Compare ARS vs. USD-linked compensation, hybrid vs. fully remote policies from Buenos Aires or Córdoba, and - crucially - how many models are actually in production, not just in slide decks. Regional analyses of nearshore hubs repeatedly show Argentina’s advantage in overlapping US time zones and strong STEM talent, but those macro strengths only matter if the specific team you join is shipping.

If you’re still at the “how do I even board this Subte?” stage, an affordable bootcamp can be your entrance turnstile. Programs like Nucamp’s AI tracks run roughly 16-25 weeks, with tuition around ARS 1,911,600-3,582,000 (about USD 2,100-4,000) and outcomes near a 78% employment rate and 4.5/5 average student rating. Their project-based paths, from AI Essentials to Solo AI Tech Entrepreneur, are designed to take you from zero to portfolio, and their regional career support connects grads to employers much like those featured in Nucamp’s own tech-employer guides.

At some point the train arrives, the doors slide open, and you have to step on. Use this list as your Subte map - but then walk the streets, talk to engineers inside these companies, sanity-check salary bands, and see whether that station feels like somewhere you’d actually live, not just transfer through.

Frequently Asked Questions