14 publicaciones de empleo destacadas de ai / ml engineer en loreto cdmx (¡Están contratando!)

Data Engineer II, DASH Device Operations
Servicios Comerciales Amazon Mexico S. de R.L. de C.V. —Ciudad de México, CDMX
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- Preparación de alimentos
- Java
Senior Manager, Data Engineering
Scribd, Inc. —Ciudad de México, CDMX
- Tiempo completo
- Administración de personal
- CAD
- SQL
- Seguro dental
- Vacaciones adicionales o permisos con goce de sueldo
Data Analyst
Tillster —Ciudad de México, CDMX
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- Trabajo híbrido
- Administración de personal
- Análisis de datos
- SQL
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Senior Software Engineer, Privacy
Lyft —Ciudad de México, CDMX
- Inglés
- Investigación
- Java
Senior Data Analyst
Delinea —Ciudad de México, CDMX
- Tiempo completo
- Azure
- Informática
- Experiencia en ventas
- Seguro de vida
9d
Senior Forward Deployed Engineer
Arkham Technologies —Ciudad de México, CDMX
- Trabajo híbrido
- Administración de personal
- SQL
- Capacitación
Postulación rápida
Senior Remediation Architect, Security Transformation Services, Mandiant
Google —Ciudad de México, CDMX
- Administración de personal
- Informática
- Inglés
Senior Engineer, DevOps Engineering
Bain & Co. —Ciudad de México, CDMX
- Tiempo completo
- Azure
- Administración de personal
- Informática
Postulación rápida
13d
Growth Delivery Director
Growth Protocol —Ciudad de México, CDMX
- Tiempo completo
- Trabajo híbrido
- Experiencia en ventas
Postulación rápida
Senior Data Engineer
SimplePractice —Ciudad de México, CDMX
- Administración de personal
- Informática
- DevOps
- Seguro dental
- Seguro de la vista
Postulación rápida
Site Reliability Engineer
Levi Strauss & Co. —Centro (Área 1), CDMX
- Tiempo completo
- Azure
- Administración de personal
- Informática
Postulación rápida
Salesforce Developer
Salesforce —Ciudad de México, CDMX
- Tiempo completo
- DevOps
- Desarrollo de software
Kubernetes DevOps Specialist
Arcadion —Ciudad de México, CDMX
- Home Office (Desde casa)
- Azure
- DevOps
- Experiencia en bases de datos
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Enterprise Account Executive (Sales)
Desteia —Ciudad de México, CDMX
- Tiempo completo
- Microsoft Excel
- Informática
- Experiencia en ventas
- Vacaciones adicionales o permisos con goce de sueldo
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Data Engineer II, DASH Device Operations

Servicios Comerciales Amazon Mexico S. de R.L. de C.V. -
Ciudad de México, CDMX

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Información del empleo

Tiempo completo

Cualificaciones

Modelado de datos
Estadísticas
Informática
Ingeniería de software
Big data
Spark
Análisis de negocios
Preparación de alimentos
Apache Hive
Java
Maestría
Bash
SQL
Licenciatura
HBase
JavaScript
Perl
Capacitación
S3
Puppet
Kafka
Redshift
Chef
Python
Shell Scripting
Debugging

Descripción completa del empleo

DESCRIPTION

We are seeking a talented, self-directed Data Engineer to design, build, and operate large-scale, high-performance data infrastructure that powers analytics, AI/ML workloads, and intelligent automation across Device Operations. You will implement data structures using best practices in data modeling and ETL/ELT processes, build real-time and batch pipelines, and enable AI-ready data foundations that support both traditional BI and emerging agentic systems. You will gather business and functional requirements and translate them into robust, scalable solutions that work within the broader data architecture. You will analyze source systems, drive best practices with partner teams, and participate in the full development lifecycle — from design and implementation to documentation, delivery, and operational support.

The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and is passionate about enabling data-driven decisions at scale. They are an expert in data modeling, ETL design, and data warehousing — and are energized by the intersection of data engineering and AI/ML, where well-structured data infrastructure creates an outsized impact on intelligent systems. They are a self-starter, comfortable with ambiguity, able to think big while paying careful attention to detail, and thrive in a fast-paced, collaborative environment.

Key job responsibilities
Design, implement, and operate scalable data pipelines (batch and real-time) that serve analytics, reporting, and AI/ML workloads

Build and maintain data infrastructure that supports AI-ready datasets — structured for consumption by machine learning models, agents, and natural language interfaces

Interface with technology teams to extract, transform, and load data from diverse sources using SQL, Python, and distributed computing frameworks

Implement data models and ETL/ELT processes using best practices in dimensional modeling, data vault, or hybrid approaches on MPP data warehouses

Build robust data integration pipelines using SQL, Python, and Spark across batch and streaming paradigms

Design and deliver high-quality datasets that support business analysis, customer reporting, and AI/ML feature engineering

Partner with scientists and application engineers to ensure data infrastructure meets the needs of ML training, inference, and agentic automation systems

Interface with business customers, gather requirements, and deliver complete, well-documented data solutions

Evaluate and make decisions around dataset designs, pipeline architectures, and tooling proposed by peer engineers

Produce comprehensive dataset documentation, metadata, and data lineage artifacts

Mentor junior data engineers on best practices in data engineering, code quality, and operational excellence

A day in the life
You will work across the full spectrum of data engineering — building pipelines that ingest from operational systems, designing warehouse schemas that serve thousands of daily queries, optimizing infrastructure for cost and performance, and enabling new AI/ML capabilities by making data accessible, reliable, and well-governed. You will collaborate with scientists, BI engineers, and application developers to solve problems that span traditional analytics and emerging intelligent systems. Some days you will be deep in SQL optimization; other days you will be designing real-time CDC pipelines or enabling a new agent to query data programmatically.

About the team
The Data, Analytics, and Science Hub (DASH) team builds scalable data platforms, analytical frameworks, AI-powered solutions, and reporting infrastructure to support Device Operations & Supply Chain. DASH serves multiple organizations across DeviceOps — delivering data engineering, business intelligence, and science capabilities that power operational decision-making. The team operates at the intersection of data infrastructure and AI innovation, building systems that serve both human analysts and intelligent agents supporting Device Ops users.

BASIC QUALIFICATIONS

Bachelor's degree in a quantitative/technical field such as computer science, engineering, statistics
4+ years of data engineering, database engineering, business intelligence or business analytics experience
Experience in writing complex, highly-optimized SQL queries across large datasets
4+ years of development/programming/scripting language (Python/Java/Bash/Perl) experience
Experience in data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
Experience in Redshift, or experience in Hive/Spark/Hbase/Yarn and experience in Kafka
Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
Knowledge of distributed systems as it pertains to data storage and computing

PREFERRED QUALIFICATIONS

Master's degree in engineering, statistics, computer science, mathematics, or a related quantitative field
Experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda, or experience with training and deploying machine learning systems to solve large-scale optimizations
Experience with infrastructure as code, ops automation, and configuration management tools such as Chef, Puppet, or Ansible
Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy
Experience as a mentor, tech lead or leading an engineering team, or experience debugging, profiling, and implementing best software engineering practices in large-scale systems
Knowledge of software engineering best practices across the development life cycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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ai / ml engineer empleos en loreto cdmx

Data Engineer II, DASH Device Operations

Senior Manager, Data Engineering

Data Analyst

Senior Software Engineer, Privacy

Senior Data Analyst

Senior Forward Deployed Engineer

Senior Remediation Architect, Security Transformation Services, Mandiant

Senior Engineer, DevOps Engineering

Growth Delivery Director

Senior Data Engineer

Site Reliability Engineer

Salesforce Developer

Kubernetes DevOps Specialist

Enterprise Account Executive (Sales)

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