Santex es una empresa global con sede en EE. UU., fundada en 1999, con 26 años de experiencia en la industria del software. Con oficinas centrales en California y en Córdoba, Argentina, su red de talento se extiende por más de 18 países gracias a su cultura flexible y de trabajo remoto. Santex se especializa en el desarrollo de software empresarial a medida, operando a través de Hubs que abarcan comercio electrónico, BIM, movilidad, distribución de contenido, integración, desarrollo web y móvil, computación en la nube, inteligencia artificial (IA), ciencia de datos, consultoría de TI y servicios. La empresa está comprometida con generar un impacto positivo en tres dimensiones: económica, social y ambiental.

Job Description: AI Specialist

Purpose: Diseñar, entrenar y mantener soluciones de inteligencia artificial, incluyendo modelos predictivos, asistentes conversacionales y productos core (como SAM Core), garantizando la viabilidad técnica de las pruebas de concepto (POC) y aportando visión técnica para estimaciones y diseño de soluciones.

Critical Tasks

• Diseño y entrenamiento de modelos AI
• Crear y ajustar modelos predictivos adaptados a las necesidades de cada prueba de concepto.
• Mantenimiento de plataformas AI
• Operar y mantener SAM, asegurando estabilidad, rendimiento y actualización continua.
• Soporte técnico para POC
• Colaborar en la construcción técnica de las pruebas, mostrando capacidades, estimando tiempos y definiendo equipos necesarios.
• Consultoría técnica interna
• Asesorar sobre tecnologías aplicables, mejores prácticas y arquitecturas para futuros proyectos.

Essential Functions

• Desarrollar soluciones backend AI
• Implementar, ajustar y mantener modelos y arquitecturas para cada proyecto.
• Estimar esfuerzos técnicos
• Analizar requisitos, calcular tiempos y recursos necesarios para garantizar la viabilidad técnica.
• Asegurar la calidad técnica
• Velar por la robustez, seguridad y rendimiento de los productos y plataformas AI.
• Colaborar con el Product Manager
• Trabajar en conjunto para alinear visión técnica y objetivos de negocio.

Professional experience:

• Experiencia desarrollando soluciones backend AI.
• Experiencia en diseño y entrenamiento de modelos predictivos.
• Plus: mantenimiento de plataformas conversacionales.

Leadership Competences:

• Visión Estratégica: Comunicar de manera clara y efectiva ideas técnicas y complejas tanto a colegas técnicos como a stakeholders no técnicos. Debe demostrar habilidades de escucha activa para poder identificar necesidades y preocupaciones
• Comunicación Efectiva: Habilidad para comunicar claramente la visión, estrategia y progreso del producto a todos los niveles de la organización.
• Trabajo en equipo: trabajar efectivamente con diversos equipos y facilitar la coordinación entre departamento
• Coraje: Promover y gestionar tensiones en pos de la disrupción. Ver los riesgos como una oportunidad.

Formación Académica:

- Profesional en Ingeniería en Sistemas, Ciencias de la Computación, Matemática, o afines.
- Formación complementaria: Cursos en machine learning, NLP, deep learning.

Languages: Español. - Inglés

Locación: CABA o Córdoba

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental.

Job Description: Machine Learning Engineer

We are looking for an experienced and driven Machine Learning Engineer to join our Advanced Analytics team. You will play a pivotal role in advancing our machine learning capabilities, focusing on the building, training, deploying, scoring, and monitoring of models for various use cases, including personalized recommenders, forecasting, and LLM modeling.

Responsibilities

• Model Development & Deployment: Develop and deploy Machine Learning, Deep Learning, and GenAI models to enhance operational efficiency and customer experience.
• Continuous Improvement: Improve models by monitoring performance, conducting A/B testing, and implementing feedback loops.
• Architecture & Scalability: Architect and rebuild complex ML frameworks from the ground up, incorporating multi-threaded processing and distributed workloads to support scalable pipelines.
• Cross-functional Collaboration: Work with engineers and product managers to integrate AI solutions into production systems within a fast-paced Agile environment.
• Operational Excellence: Own production ML systems, participating in on-call rotations, troubleshooting incidents, and maintaining overall model reliability.
• Innovation: Stay updated with the latest advancements in AI to ensure our solutions remain cutting-edge.

Requirements

• Education: Bachelor’s or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
• Experience: 3+ years of experience designing, building, and operating production-scale machine learning systems.
• Technical Mastery: Expert-level programming in Python with extensive experience using PySpark and distributed data platforms (e.g., Databricks) for large-scale datasets.
• Deep Learning & Frameworks: Strong experience with Scikit-Learn, MLlib, and PyTorch, including regression, time series, clustering, and deep learning.
• Distributed Systems: Deep understanding of Spark execution models, partitioning strategies, shuffle optimization, and vectorized processing (Pandas UDFs).
• Data & Cloud: Strong SQL expertise and experience deploying ML workloads to AWS (EC2, S3, DynamoDB, Lambda).
• Ops: Experience implementing MLOps / LLMOps practices.

Key Competencies

• Ownership: Ability to manage strategic initiatives in a rapidly evolving QSR environment.
• Adaptability: Eagerness to learn and adapt in ambiguous problem spaces with a collaborative attitude.
• Outcome-Oriented: Focus on analyzing and visualizing data to drive continuous improvement across the business.
• Professionalism: Maintain transparency and professionalism in communication within the team and with stakeholders.

📍 Location

• Flexible / Remote options available.

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental.

Job description:
We are seeking an AI Data Engineer to design, build, and scale the data foundations that power AI, advanced analytics, and natural language interfaces across a global Quick Service Restaurant (QSR) enterprise. This is a high-impact role at the intersection of data engineering and applied AI, where the pipelines you build become the building blocks for use cases like automated insight generation, conversational analytics, and intelligent decision support at restaurant scale. A core focus of this role is data ingestion from POS systems and back-office vendor platforms spanning labor, inventory, finance, and supply chain. You will ensure reliable, standardized, and scalable ingestion of high-volume, multi-source data into the enterprise data platform, transforming fragmented operational data into AI-ready assets. You will also play a critical role in connecting raw data to business meaning, supporting governed KPI definitions and AI-driven context engineering through a robust semantic layer that enables both human analysts and AI agents to query, reason over, and act on trusted data.

Key Responsibilities – Data Ingestion & Integration (POS & Back Office)
- Design and build ingestion pipelines for POS systems across global markets (transactions, orders, payments, speed of service)
- Integrate data from back-office vendor systems (labor scheduling, inventory, supply chain, finance)
- Handle diverse ingestion patterns (batch, streaming, APIs, file-based ingestion)
- Normalize and standardize data across vendors, regions, and brands
- Ensure data freshness, completeness, and consistency across all ingestion pipelines
- Manage vendor-specific schemas and evolving data contracts

Key Responsibilities – Data Platform & Pipeline Engineering
- Build scalable ELT/ETL pipelines using modern tools (DBT, Airflow, etc.)
- Support real-time and batch processing for operational and analytical use cases
- Optimize ingestion pipelines for performance, reliability, and cost
- Ensure high availability across global data workloads

Key Responsibilities – Semantic Layer Enablement
- Build data models supporting standardized KPIs (same-store sales, speed of service, traffic, labor productivity)
- Align ingested raw data with business-friendly semantic abstractions
- Support both governed (manual) and AI-driven semantic approaches

Key Responsibilities – Metadata & Context Engineering
- Capture metadata, lineage, and schema details from ingestion pipelines
- Enable AI systems using structured context (metadata, lineage, example queries)
- Ensure AI systems can reliably interpret POS and operational data

Key Responsibilities – Governance & Data Quality
- Implement validation and monitoring for ingestion pipelines
- Ensure data accuracy across POS and vendor systems
- Support governance for consistent metric definitions across regions
- Maintain versioning and schema evolution processes

Required Qualifications
- 5+ years of experience in data engineering
- Strong SQL and Python skills
- Experience building ingestion pipelines from APIs, streaming, and batch sources
- Experience with POS or operational data systems preferred
- Experience with cloud data platforms and modern data stack

Preferred Qualifications
- Familiarity with Agentic development tools such as Codex, Cursor or Claude Code
- Experience in QSR or retail environments
- Experience integrating vendor systems (labor, inventory, finance)
- Familiarity with semantic layers and metadata tools
- Experience enabling data for AI/LLM use cases

Success Metrics
- Reliability and latency of POS and back-office data ingestion
- Standardization across vendor data sources
- Reduction in data inconsistencies
- Improved downstream analytics and AI performance

Summary
This role ensures that high-quality, standardized data from POS and back-office systems flows into the enterprise platform—enabling trusted analytics, semantic consistency, and AI-driven insights at global scale.

Location: LATAM

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental.

Job Description:

We are seeking an AI Enablement Engineer to design and scale the semantic layer that turns raw restaurant data into a shared language for AI, analytics, and decision-making across a global Quick Service Restaurant (QSR) enterprise. This is a foundational role in our AI strategy: the semantic layer you build is what makes natural language analytics, automated reporting, and intelligent AI agents possible at enterprise scale. Your focus is bridging business meaning and data, ensuring that AI systems, analytics platforms, and end users consistently interpret critical metrics like same-store sales, speed of service, traffic, and labor productivity in a standardized, governed way. When an AI agent or a franchisee asks "how did my restaurant perform last week," the accuracy and trust of that answer starts with your work.
You will define and operationalize a hybrid semantic layer strategy that combines governed, metadata-driven models with AI-powered context engineering to enable scalable, accurate, and self-service insights across restaurants, corporate teams, and franchisees, unlocking the building blocks for conversational analytics and autonomous AI workflows.

Key Responsibilities – Semantic Layer Strategy & Architecture

• Define and implement an enterprise semantic layer that standardizes metric definitions and aligns business questions to data
• Establish canonical definitions for core KPIs (e.g., same-store sales, speed of service, traffic, labor productivity)
• Design mapping between business concepts and underlying data models across systems (POS, digital, supply chain, finance)
• Provide structured metadata to power AI systems, copilots, and analytics tools. 

Key Responsibilities – Hybrid Semantic Model Implementation

• Build and maintain manual, metadata-driven semantic models for high-criticality domains (finance, core KPIs, franchise reporting)
• Enable AI-driven context engineering for exploratory analytics, operational insights, and ad-hoc queries
• Define how both approaches coexist in a unified architecture

Key Responsibilities – Tooling & Platform Enablement

• Implement semantic layer tooling (DBT, semantic views, YAML/Markdown metadata, data catalogs like Atlan)
• Enable downstream consumption by BI tools, APIs, and AI applications
• Create reusable abstractions and templates to scale globally

Key Responsibilities – AI & Context Engineering Integration

• Enable AI systems (LLMs, agents, copilots) to leverage semantic metadata
• Support context engineering using data lineage, metadata, example queries, and repository context
• Improve AI reliability by grounding outputs in governed definitions

Key Responsibilities – Governance & Data Quality

• Establish governance processes for metric definitions
• Ensure alignment across regions, brands, and franchise reporting
• Implement validation, versioning, and change management
• Define guardrails to prevent inaccurate analytics outputs

Key Responsibilities – Adoption & Enablement

• Partner with business and technical teams to drive adoption
• Educate stakeholders on standardized metrics and self-service analytics
• Provide documentation, playbooks, and training

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Analytics, or related field
• 5+ years of experience in data engineering, analytics engineering, or AI/ML systems
• Strong experience with semantic modeling or analytics engineering
• Proficiency in SQL and Python
• Experience with DBT and modern cloud data platforms
• Understanding of APIs and distributed systems

Preferred Qualifications

• Experience in QSR, retail, or franchise environments
• Experience with semantic/metrics layers
• Familiarity with data catalogs (e.g., Atlan)
• Experience with generative AI, LLMs, and context engineering
• Knowledge of data lineage and governance frameworks

Additional Expectations

• Design and maintain CI/CD pipelines, including automated builds, testing, and deployment of solutions
• Develop and execute functional and regression test suites to validate metric accuracy, data completeness, and semantic model behavior across environments
• Implement automated data and code quality checks and integration tests within the deployment pipeline to catch issues before they reach production
• Apply software engineering best practices including version control, code review, branching strategies, and environment promotion (dev/staging/prod) to all semantic layer and metadata assets
• Monitor platform performance, troubleshoot pipeline failures, and maintain observability across semantic layer infrastructure using logging, alerting, and health checks
• Define and manage infrastructure as code (e.g., Terraform, CloudFormation) for data platform resources, ensuring reproducible and auditable environment provisioning across development, staging, and production
• Implement and maintain role-based access controls, secrets management, and security policies across semantic layer tooling, data platform resources, and API integrations to ensure compliance with enterprise security standards

Key Skills & Competencies

• Systems thinking across data, analytics, and AI
• Ability to translate business metrics into data models
• Balance governance with scalability
• Strong stakeholder management
• Clear communication of complex concepts

Success Metrics

• Adoption of standardized metric definitions
• Reduction in reporting inconsistencies
• Improved accuracy of AI-generated insights
• Increased self-service analytics usage
• Faster time-to-insight

Reporting Structure

• Reports to: VP / Director of Data, AI, or Platform Engineering
• Works closely with: Data Engineering, Analytics, AI/ML, Finance, Operations, Franchise teams

Summary
The AI Enablement Engineer (Semantic Layer) establishes a shared language between business and data. By combining governed semantic models with AI-driven context understanding, this role enables scalable, accurate, and trusted analytics and AI across a global QSR enterprise.

Location: LATAM