Top 10 Companies Hiring AI Engineers in Ecuador in 2026

Table of Contents

• How to read this Top 10
• Banco Pichincha
• Globant
• Kushki
• Corporación Favorita
• Banco de Guayaquil
• Accenture
• Claro Ecuador
• CNT EP
• Kin Analytics
• Banco del Pacífico
• Build your own starting XI
• Frequently Asked Questions

Banco Pichincha

On Ecuador’s national “AI canchita,” Banco Pichincha plays the reliable center-back: not the flashiest moves, but huge responsibility and constant minutes. As the country’s largest private bank, it has quietly assembled a 50+ person Data & Analytics Center of Excellence that ships models into production, not just slide decks.

What you actually build here

AI and ML engineers work on pipelines that sit directly in the path of money and customers, including:

• Real-time fraud detection across millions of daily card and digital transactions
• Credit risk models that score underbanked clients with thin files
• Spanish-language LLM-based virtual assistants that absorb everyday Ecuadorian slang

Because these systems touch core banking, you see clear metrics - fraud losses, default rates, NPS - move when your model does.

Stack, workflows, and how they hire

The stack is modern by Latin American banking standards: Python with TensorFlow and Scikit-learn for modeling, Apache Spark for large-scale feature engineering, and an AWS backbone using Amazon SageMaker plus Kubeflow for MLOps and orchestration. That means end-to-end exposure from ingestion to deployment and monitoring.

Vacancies are funneled through the dedicated Talento Pichincha careers portal, where you’ll often see roles labeled as Data Scientist, ML Engineer, or Analytics Specialist tied to risk, fraud, or digital channels.

Salaries, profile fit, and getting in

Compensation is strong for a nationally anchored employer: entry AI/ML roles land around $1,800-$2,500/month, while senior engineers typically earn $4,500-$6,500/month. In Ecuador’s dollarized economy, that puts Banco Pichincha well above many local tech averages highlighted in Nucamp’s overview of the best-paid tech jobs in Ecuador.

The bank regularly sources talent from universities like USFQ and from professionals who’ve strengthened their Python, SQL, and cloud foundations through intensive programs such as Nucamp’s 16-week Back End, SQL and DevOps with Python bootcamp (tuition US$2,124). If you want deep, long-term experience in financial AI and MLOps while staying rooted in Ecuador, this is one of the safest - and most technically serious - places to play.

Globant

Where Banco Pichincha feels like a disciplined center-back, Globant is the winger sprinting up Ecuador’s AI touchline: fast, creative, and plugged into games far beyond the national league. From hubs in Quito and Guayaquil plus remote teams across the country, Globant’s AI Studio treats Ecuador as a strategic node in its global delivery network.

Global AI projects from a local base

After announcing it would hire 200+ employees in its first year in Ecuador, Globant began staffing teams that build:

• GenAI copilots and chatbots for Fortune 500 clients
• Recommendation systems and personalization engines for media and retail
• Computer vision and NLP modules embedded in large digital platforms

These initiatives extend the same “AI Pods” model described on Globant’s global AI practice pages, where cross-functional squads own features end to end, from ideation to production.

Stack, pods, and day-to-day reality

The technical environment is diverse but modern: PyTorch and TensorFlow for deep learning, LangChain and vector databases for GenAI, and a strong bias toward Google Cloud Vertex AI with multi-cloud exposure. You work in “Pods” with other ML engineers, backend developers, product owners, and designers, often syncing daily with teams in North America or Europe.

Engineers frequently mention in Glassdoor reviews of working at Globant that the culture is remote-first and flexible, but also demanding in terms of client communication and quick context switches.

Pay, positioning, and fit

Mid-level AI engineers typically earn around $3,000-$4,500/month, while Lead and Principal roles can reach roughly $6,000-$8,500/month. In Ecuador’s dollarized economy, that’s strong compensation, especially given the lower cost of living compared to US or European tech hubs.

If you want to stay in Ecuador while building for global platforms, are comfortable presenting to clients, and enjoy jumping between domains, Globant is one of the most dynamic “attacking” options on the local AI field.

Kushki

In Ecuador’s fintech league, Kushki plays the high-pressing striker: it chases every loose ball of transactional data and turns milliseconds into money. As a regional payment gateway with strong engineering teams in Quito and Guayaquil, its AI systems sit directly on top of live authorization streams.

What you ship in production

Every model you deploy shows up quickly in merchant dashboards and loss reports. Typical workloads include:

• Transactional risk scoring that flags fraud in real time
• Automated KYC onboarding using computer vision for ID validation
• Smart routing to choose the cheapest, lowest-latency payment rails per transaction

Small improvements in precision or latency translate into higher approval rates and lower fraud costs across Latin America.

Stack, interviews, and tempo

Kushki leans heavily on cloud-native, event-driven architectures: Python (often with FastAPI) for services, AWS Lambda and SageMaker for training and serverless inference, Databricks for analytics and feature pipelines, and Terraform for Infrastructure-as-Code. These tools mirror the most in-demand AI stacks identified in Latin American AI salary and tooling reports.

Interviews emphasize system design plus LeetCode-style coding, reflecting the need to handle both models and high-throughput distributed systems. Expect on-call rotations and incident reviews when something odd happens in production risk scores.

Compensation, profile, and how to prepare

For AI roles, mid-level engineers typically see around $3,500-$5,000/month, while senior profiles reach roughly $5,500-$7,500/month - among the highest fintech ranges in Ecuador’s dollarized market.

The bar is much easier to clear if you already blend Python, SQL, and cloud fundamentals with product thinking. Intensive programs like Nucamp’s 16-week Back End, SQL and DevOps with Python bootcamp (tuition around US$2,124) or its 25-week Solo AI Tech Entrepreneur path (about US$3,980) give career changers from cities like Quito, Guayaquil, and Cuenca a structured way to build those skills before stepping into Kushki’s high-tempo game.

Corporación Favorita

In Ecuador’s AI midfield, Corporación Favorita is the engine that never stops running. Behind Supermaxi, Megamaxi and its other brands sits one of the country’s deepest retail datasets, and a data science team focused on turning that into fewer stockouts and less waste across hundreds of stores.

Where AI shows up in the supermarket

Most work here is classic applied machine learning at scale:

• Demand forecasting for 10,000+ SKUs across diverse regions and store formats
• Inventory optimization to cut stockouts and shrinkage while keeping shelves full
• Personalized offers and pricing inside the MAXI loyalty ecosystem

A small lift in forecast accuracy can mean millions saved in logistics and spoilage, so business impact is very tangible.

Tools, integrations, and daily work

The environment blends enterprise systems with open-source tools: Python and R for modeling, heavy integration with SAP HANA, and batch plus near-real-time data pipelines feeding supply-chain and marketing decisions. You spend much of your time translating RMSE and feature importance into operational terms like “days of coverage” and “waste percentage.”

This kind of large-scale, production retail AI mirrors the applied focus that regional consultancies featured on Clutch’s list of Ecuador AI developers highlight as a core strength of the local market.

Salaries, fit, and who thrives here

Senior AI/ML roles typically pay around $4,000-$5,500/month, in line with high-end national ML benchmarks. You’re unlikely to work on flashy demos, but you will see your models reflected in real trucks, real shelves, and real customers across Ecuador.

If you like time-series forecasting, optimization problems, and tight feedback loops with operations teams rather than external clients, Corporación Favorita is a very strong, often underrated choice for an AI career anchored in Ecuador’s real economy.

Banco de Guayaquil

On Ecuador’s AI field, Banco de Guayaquil plays the creative “10”: less about pure risk defense, more about threading personalized passes through every digital channel. Its AI work centers on making banking feel tailored, not generic, while still running on serious enterprise infrastructure.

Customer-centric AI in production

Most AI projects here sit close to the customer:

• Next Best Action (NBA) engines that suggest the right product, message, or nudge in-app or via advisors
• Churn prediction models focused on retaining high-value segments before they drift away
• Sentiment analysis over feedback, call transcripts, and social channels, tuned to coastal and Sierra slang

These systems support the bank’s “Human Banking” strategy: AI surfaces recommendations and insights, but human advisors still take the final shot with clients across Ecuador.

Why Azure matters here

The bank is firmly in the Microsoft ecosystem: Azure Machine Learning for training and deployment, SQL Server and Azure SQL as the data backbone, and Power BI for real-time dashboards. This maps cleanly to what regional hiring guides like Digiqt’s analysis of Azure AI engineers in Latin America describe as a premium skillset: cloud-native MLOps plus strong data warehousing fundamentals.

For an engineer trained on Python, SQL, and cloud (for example through a structured path like Nucamp’s AI or back-end programs), this stack offers a straightforward path into production-grade personalization.

Hiring patterns, salaries, and who fits

Banco de Guayaquil recruits heavily from ESPOL, Universidad de Cuenca, and experienced engineers from the wider Ecuadorian market, often competing with remote-friendly roles listed on platforms such as Jobgether’s Ecuador data and analytics jobs.

While exact bands are less public than at some fintechs, compensation for specialized AI roles is generally around or above the national ML engineer average of ~$50,000/year, with room to grow as you take on more responsibility in experimentation and model governance. If you like Azure, experimentation in production, and customer analytics more than deep credit modeling, this is one of the best “playmaker” roles on Ecuador’s AI pitch.

Accenture

Some players don’t belong to a single club; they move between teams, shaping how everyone else plays. In Ecuador’s AI ecosystem, Accenture fills that role: a consulting powerhouse helping banks, telcos, retailers, and public institutions design and deliver AI systems rather than owning just one product.

What you build across clients

From its Ecuador operations, Accenture’s AI engineers typically rotate through projects such as:

• End-to-end recommendation engines and demand forecasting for retailers and CPGs
• NLP assistants and workflow automation for banks and insurers
• Network analytics and churn modeling for telecom and media clients

This mirrors the broader portfolio described in Accenture’s global AI practice, but executed with local and regional stakeholders in Spanish.

Stack, patterns, and consulting cadence

Technical stacks are aggressively multi-cloud: projects on AWS, Azure, and GCP; Databricks for lakehouse architectures; and strong emphasis on governance, monitoring, and security. You’ll spend as much time designing data contracts and MLOps pipelines as you do tuning models.

The rhythm is classic consulting: discovery workshops, rapid PoCs, then industrialization. Case-style interviews test whether you can translate messy business goals into measurable AI use cases, echoing patterns highlighted in AI engineering hiring trend analyses on Medium.

Compensation and who thrives here

Mid-to-senior AI engineers in Ecuador typically see around $3,500-$5,500/month, with variation by project load and seniority. Instead of depth in one domain, you gain breadth: banking one quarter, telco the next, public sector after that.

If you enjoy context-switching, stakeholder workshops, and learning how different industries across Ecuador apply AI, Accenture is the “tactical coach” that will stretch both your technical and consulting skills.

Claro Ecuador

Claro Ecuador, part of América Móvil, is the box-to-box midfielder of the country’s AI scene: always running, rarely glamorous, but absolutely essential. With millions of mobile and broadband subscribers, its models influence call quality, data speeds, and customer support experiences from the Costa to the Sierra.

Where AI touches the network

Data problems here are large-scale and relentlessly operational. Typical AI workloads include:

• Predictive maintenance on cell towers using geospatial and time-series data to prevent outages
• Network traffic optimization to reduce congestion, dropped calls, and slow data in peak hours
• Customer-facing AI chatbots and virtual agents for billing, tech support, and plan changes

Every small gain in model accuracy or latency gets amplified across millions of interactions nationwide.

Stack, big data, and skills

Telecom means serious big data. Claro leans on the Hadoop ecosystem (HDFS, Hive, Spark) for large-scale analytics, with Java and Python for ETL and modeling, and tight integration into legacy OSS/BSS systems. Engineers report that interviews emphasize strong SQL, geospatial reasoning, and practical business cases over exotic architectures.

The blend of Python, SQL, and cloud-friendly thinking makes telco AI a natural next step for Ecuadorian engineers who have built foundations through intensive training in back-end, DevOps, or AI-focused bootcamps, then want to operate at true national scale.

Compensation, alternatives, and who fits

AI and data roles at Claro generally sit in the mid-range of Ecuador’s ML salary spectrum: not as high as fintech “unicorn” levels, but competitive for stable, large-enterprise work with clear career ladders. For comparison, global clients regularly seek Ecuador-based AI talent on platforms like Upwork’s marketplace for AI engineers in Ecuador, underscoring the value of these big-data skills.

If you enjoy massive datasets, geospatial analysis, and AI systems where uptime matters more than demos, Claro offers one of the strongest paths into applied AI engineering in the national telecom backbone.

CNT EP

If Claro is the private-sector workhorse, CNT EP is Ecuador’s national defender: responsible for keeping the whole country connected, from dense urban barrios to remote Andean communities. As the public telecom operator, its data and AI work often sits at the intersection of engineering, policy, and national coverage goals.

Nation-scale AI problems

AI-related projects at CNT EP typically revolve around infrastructure and security rather than consumer apps. Common workloads include:

• Big data analytics on network logs to guide long-term infrastructure investment
• Geospatial models for planning fiber and mobile expansion in rural and urban areas
• Cybersecurity analytics to detect anomalies, intrusions, and fraud across core systems

Some of these efforts are run in collaboration with government entities, giving your models a visible impact on national connectivity and digital inclusion.

Stack, projects, and hiring pipeline

Technically, you’ll work with large-scale data platforms for logs and events, geospatial tools for mapping and simulation, and increasing use of ML for anomaly detection and capacity planning. CNT’s evolution as a telecom operator is documented in profiles such as DevelopmentAid’s overview of Telecsa/CNT Mobile, highlighting its role in Ecuador’s ICT backbone.

Hiring has a strong public-university flavor, especially from Escuela Politécnica Nacional (EPN) and other state institutions, with structured entrance processes and longer-term career paths.

Salaries, tradeoffs, and who it fits

Entry-level technical AI/data roles usually start around $1,500-$2,000/month. That’s below fintech or global consultancies, but compensation is offset by job stability, benefits, and the chance to work on infrastructure that literally keeps Ecuador online.

If you’re motivated by public impact, geospatial and network-scale problems, and a predictable career path over startup volatility, CNT EP is a strong early-career home base - and a unique vantage point on how AI shapes national telecom policy and operations.

Kin Analytics

Kin Analytics is the specialist playmaker in Ecuador’s AI lineup: a boutique consultancy that doesn’t try to do everything, but goes very deep in a few high-leverage domains. From its base in Quito, it works with professional football clubs and financial institutions across the region, turning raw data into decisions that affect scoreboards and balance sheets.

Two verticals, one analytics core

Most Kin projects fall into two buckets:

• Sports analytics: player performance metrics, injury-risk indicators, opponent analysis, and valuation models that help clubs manage transfers and lineups
• Financial analytics: AI-powered credit scoring, portfolio risk models, and collections optimization for fintechs and lenders

This mix gives you rich time-series problems (matches, training, sensor data) alongside tabular risk modeling at meaningful financial scale.

Stack, cloud, and client-facing work

Technically, Kin is very Python-centric, with heavy use of Azure for cloud infrastructure and deployment, plus custom dashboards and visualization tools tailored to non-technical users like coaches or risk managers. A typical week might include cleaning tracking data from a weekend match, building a new feature pipeline for a credit model, and then presenting findings to executives.

Public postings like the Senior AI/ML Engineer role in Quito highlight expectations around end-to-end ownership: from scoping and data engineering to modeling and communication.

Compensation and who thrives here

Comp is competitive with mid-to-senior AI roles in the Quito market, particularly for profiles strong in Python and Azure. You won’t have the headcount scale of a bank, but you’ll get more direct impact and visibility per project.

If you’re excited by sports analytics, credit risk, and close collaboration with clients instead of internal-only stakeholders, Kin Analytics is one of the most interesting “number 10” roles in Ecuador’s AI ecosystem.

Banco del Pacífico

Banco del Pacífico is the disciplined holding midfielder in Ecuador’s banking lineup: state-owned, highly regulated, and laser-focused on making digital channels safer and more accessible. Its AI initiatives are less about pushing the flashiest models and more about building systems that regulators, auditors, and customers can trust.

Where AI shows up in their products

Most AI work here sits inside core customer journeys and security layers, including:

• Virtual assistants that use NLP to handle routine questions in web and mobile banking
• Automated loan approvals that combine rules engines with ML models to score applications faster
• Biometric security (voice, face, fingerprint) to strengthen authentication and reduce fraud

Because the bank is state-owned, these systems are built with strong expectations around audit trails, explainability, and fairness across different regions and customer segments in Ecuador.

Enterprise stack and model governance

On the tooling side, Banco del Pacífico leans into enterprise AI platforms. Many NLP workloads run on IBM Watson, with general ML models and hosting on Microsoft Azure and tight integrations into core banking systems and mobile apps. This combination reflects how IBM positions Watson for regulated industries on its enterprise AI product pages: pre-built components plus strong governance features.

Day to day, AI engineers spend as much time on documentation, monitoring, and compliance reviews as they do on model tuning, working closely with risk, legal, and cybersecurity teams.

Compensation, stability, and who it suits

Salaries for permanent staff follow public-sector-influenced scales, but specialized AI roles and external “consultor” contracts can command competitive project-based rates, especially for profiles with security and governance experience. The tradeoff is clear: slightly lower upside than aggressive fintechs, in exchange for stability and long-term projects that affect digital inclusion nationwide.

If you care about building AI systems that can survive regulatory scrutiny, support government-aligned programs, and help more Ecuadorians trust digital banking, Banco del Pacífico is a strong, purpose-driven option in the national AI ecosystem.

Build your own starting XI

On any neighborhood canchita in Ecuador, the captains eventually stop arguing about who’s “best” and start asking a sharper question: who plays where? Your AI career works the same way. This Top 10 isn’t a trophy ceremony; it’s a lineup of roles you can plug into across the country.

Decide your position first

Before picking a company, choose the role you want to play:

• MLOps / platforms: banks and telcos that run complex pipelines in production
• GenAI & product: consultancies and global service firms building LLM features for clients
• Applied analytics: retailers, public telcos, and niche consultancies close to operations
• Fintech risk & payments: high-intensity environments where every prediction affects money flow

Then map each company in this list to a position in your own “formation” instead of chasing a single, universal ranking.

Read the field: salaries, demand, and odds

From anywhere in Ecuador - a Quito apartment with fibra óptica or a Guayaquil coworking space - strong AI engineers can realistically earn around $3,000-$7,000/month at top employers, in a dollarized economy with living costs well below US hubs. But competition is real: analyses of AI postings show that desirable roles can attract 200-500 applications in days, a pattern echoed in videos like “My Honest Thoughts on AI and the Job Market in 2026”.

Turn the list into a playbook

To convert this ranking into action, treat it as a scouting report:

• Pick a primary stack and cloud (AWS, Azure, or GCP) and go deep
• Use structured paths - university programs, Nucamp bootcamps, or self-study - to close concrete skill gaps
• Shortlist 3-5 companies that match your position and values, not just the highest salaries
• Build projects that mirror their use cases, using real tools and patterns you see in profiles on sites like Goodfirms’ Ecuador tech company directory

In the end, the “best” company is the one where your skills, ambitions, and Ecuador’s AI ecosystem line up - and where you can step onto the field knowing exactly which role you came to play.

Frequently Asked Questions