56 Ofertas de Ingeniero Ia en Mexico

We're looking for a Machine Learning Engineer to join our growing Data & AI team. In this role, you'll help design, build, and deploy intelligent models and ML-powered workflows that solve real-world challenges at scale. You'll work closely with data engineers, product owners, and AI specialists to turn ideas into production-ready solutions, embedding AI seamlessly into business processes.
What You'll Do:

• Model Development: Design, train, and refine machine learning models that tackle real business problems, ensuring they scale effectively in production environments.
• Data Pipeline Engineering: Build and maintain robust data ingestion, preprocessing, and transformation pipelines for diverse data sources (structured and unstructured).
• AI Workflow Integration: Contribute to end-to-end ML workflows—from serving and monitoring models to evaluating and iterating on their performance.
• Advanced AI Techniques: Apply state-of-the-art approaches, including transformers, LLMs, RAG, embeddings, vector databases, predictive modeling, and reinforcement learning, to push the boundaries of what's possible.
• Model Monitoring & Optimization: Support ongoing evaluation and tuning of models to improve accuracy, efficiency, and reliability in production.
• MLOps: Help establish best practices for CI/CD, testing, and automated deployment of AI models.
• Agile Collaboration: Partner effectively with cross-functional teams in an agile setting, contributing to sprint planning, reviews, and collaborative problem-solving.
  

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
• 3+ years of experience in machine learning engineering, applied AI development, or a similar role.
• Strong, hands-on experience with ML frameworks such as TensorFlow or PyTorch, from prototyping to deployment.
• Familiarity with cloud platforms (AWS, Azure, or Databricks) and experience delivering solutions at scale.
• Solid understanding of working with large, complex datasets spanning structured and unstructured formats.
• Sharp analytical and problem-solving skills with attention to data quality and model performance metrics.
• Strong communication and collaboration abilities—you're a team player who can explain technical concepts clearly and drive projects forward.
  

We're looking for a Machine Learning Engineer to join our growing Data & AI team. In this role, you'll help design, build, and deploy intelligent models and ML-powered workflows that solve real-world challenges at scale. You'll work closely with data engineers, product owners, and AI specialists to turn ideas into production-ready solutions, embedding AI seamlessly into business processes.
What You'll Do:

• Model Development: Design, train, and refine machine learning models that tackle real business problems, ensuring they scale effectively in production environments.
• Data Pipeline Engineering: Build and maintain robust data ingestion, preprocessing, and transformation pipelines for diverse data sources (structured and unstructured).
• AI Workflow Integration: Contribute to end-to-end ML workflows—from serving and monitoring models to evaluating and iterating on their performance.
• Advanced AI Techniques: Apply state-of-the-art approaches, including transformers, LLMs, RAG, embeddings, vector databases, predictive modeling, and reinforcement learning, to push the boundaries of what's possible.
• Model Monitoring & Optimization: Support ongoing evaluation and tuning of models to improve accuracy, efficiency, and reliability in production.
• MLOps: Help establish best practices for CI/CD, testing, and automated deployment of AI models.
• Agile Collaboration: Partner effectively with cross-functional teams in an agile setting, contributing to sprint planning, reviews, and collaborative problem-solving.
  

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
• 3+ years of experience in machine learning engineering, applied AI development, or a similar role.
• Strong, hands-on experience with ML frameworks such as TensorFlow or PyTorch, from prototyping to deployment.
• Familiarity with cloud platforms (AWS, Azure, or Databricks) and experience delivering solutions at scale.
• Solid understanding of working with large, complex datasets spanning structured and unstructured formats.
• Sharp analytical and problem-solving skills with attention to data quality and model performance metrics.
• Strong communication and collaboration abilities—you're a team player who can explain technical concepts clearly and drive projects forward.
  

We're looking for a Machine Learning Engineer to join our growing Data & AI team. In this role, you'll help design, build, and deploy intelligent models and ML-powered workflows that solve real-world challenges at scale. You'll work closely with data engineers, product owners, and AI specialists to turn ideas into production-ready solutions, embedding AI seamlessly into business processes.
What You'll Do:

• Model Development: Design, train, and refine machine learning models that tackle real business problems, ensuring they scale effectively in production environments.
• Data Pipeline Engineering: Build and maintain robust data ingestion, preprocessing, and transformation pipelines for diverse data sources (structured and unstructured).
• AI Workflow Integration: Contribute to end-to-end ML workflows—from serving and monitoring models to evaluating and iterating on their performance.
• Advanced AI Techniques: Apply state-of-the-art approaches, including transformers, LLMs, RAG, embeddings, vector databases, predictive modeling, and reinforcement learning, to push the boundaries of what's possible.
• Model Monitoring & Optimization: Support ongoing evaluation and tuning of models to improve accuracy, efficiency, and reliability in production.
• MLOps: Help establish best practices for CI/CD, testing, and automated deployment of AI models.
• Agile Collaboration: Partner effectively with cross-functional teams in an agile setting, contributing to sprint planning, reviews, and collaborative problem-solving.
  

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
• 3+ years of experience in machine learning engineering, applied AI development, or a similar role.
• Strong, hands-on experience with ML frameworks such as TensorFlow or PyTorch, from prototyping to deployment.
• Familiarity with cloud platforms (AWS, Azure, or Databricks) and experience delivering solutions at scale.
• Solid understanding of working with large, complex datasets spanning structured and unstructured formats.
• Sharp analytical and problem-solving skills with attention to data quality and model performance metrics.
• Strong communication and collaboration abilities—you're a team player who can explain technical concepts clearly and drive projects forward.
  

Rol: Machine Learning Engineer

Descripción del puesto:

• Experiencia de 4 años como Machine Learning Data Engineer.
• Experiencia previa como Ingeniero de Datos desarrollando con Python y Spark.
• Desarrollo de modelos de Machine Learning utilizando bibliotecas como Scikit-Learn.
• Experiencia en creación de imágenes con Docker.
• Automatización de flujos de trabajo mediante Airflow y Kubeflow.
• Experiencia en desarrollo de endpoints.
• Conocimientos en buenas prácticas de MLOps.
• Dominio de SQL Server.

Funciones:

• Desarrollar y mantener pipelines de ML altamente automatizados para entrenamiento, validación e implementación de modelos a gran escala.
• Colaborar con equipos de ingeniería de software para integrar flujos de trabajo de ML en el ciclo de desarrollo, aplicando prácticas de DevOps y MLOps.
• Implementar y gestionar infraestructura de computación distribuida y herramientas de orquestación para soportar flujos de trabajo en producción.
• Desarrollar métricas y herramientas de monitoreo para evaluar el rendimiento y la calidad de los modelos en producción.
• Automatizar tareas de mantenimiento y monitoreo para garantizar estabilidad y confiabilidad.
• Identificar necesidades de datos y fuentes, atendiendo a los procesos de negocio y la arquitectura de Data Analytics.
• Proponer estrategias de integración de datos que aporten valor a los productos y herramientas.
• Documentar áreas de mejora en la integración de datos para optimizar procesos de transformación.
• Interpretar datos y plasmarlo en documentación para generar insights valiosos que apoyen la toma de decisiones.

· Locación: CDMX, Modalidad híbrida (2 días en oficina)

• Inglés: Intermedio

· Enlace a la oferta

· Acerca de Softtek: Fundada en 1982, Softtek es un proveedor global de servicios de TI con presencia en Norteamérica, Latinoamérica, Europa y Asia. Cuenta con 15 Centros de Desarrollo Global en EE.UU., México, China, Brasil, Argentina, Costa Rica, España, Hungría e India. Mejora resultados para grandes empresas en más de 20 países. Para más información, visita

Employment type:

B2B

Operating mode:

Remote

Location:

Mexico

We help companies gain a competitive edge by delivering customized AI solutions. Our mission is to empower our clients to unlock the full potential of AI.

We are specialized in key technologies such as LLM & RAG, MLOps, Edge Solutions, Computer Vision, and Natural Language Processing.

Our team of 120 world-class AI experts has worked on 200+ commercial and R&D projects with companies such as Unstructured, Google, Brainly, DocPlanner, B-Yond, Zebra Technologies, Hexagon, and many more.

• Team Strength – sharing and exchanging knowledge is key to our daily work
• Accountability – we take responsibility for the tasks entrusted to us so that ultimately the client receives the best possible quality
• Balance – we value work-life balance
• Commitment – we want you to be fully part of the team
• Openness – we don’t want you to be locked into one solution, we want to look for alternatives, explore new possibilities

Join our dynamic team as a Machine Learning Engineer and embark on a journey of innovation at the intersection of data science and cloud computing. We are seeking a talented individual who is passionate about leveraging cutting-edge technologies to drive business insights and solutions. If you’re excited about pushing the boundaries of what’s possible with GenAI, we invite you to be part of our team of experts!

• Collaborate with data scientists and software engineers to integrate machine learning solutions into cloud-based applications.
• Continuously optimize and improve AI algorithms for performance and accuracy in a cloud environment.
• Automate and optimize model deployment following MLOps best practices.
• Engage in the development of cutting-edge Kubernetes-driven infrastructure.
• Work on system reliability and backend stability, always looking for details to be improved.
• Share knowledge through talks and workshops (internal and external).

• Bachelor’s or advanced degree in Computer Science or Engineering.
• Proven experience (3+ years) in software engineering, including experience with Python, Bash, Git, as well as Cloud services and Linux.
• Proven experience (1+ years) in working with cloud (AWS/Azure preferred).
• Good understanding of system architecture (microservices, monoliths, REST API, DNS, caching).
• Familiarity with Docker, Kubernetes, and cloud platforms for ML deployment.
• Strong Python skills and familiarity with other object-oriented languages.
• Very effective communication skills, both written and verbal.
• Ability to solve problems and communicate complex ideas effectively.

• Basic understanding of machine learning algorithms.
• Keen interest in Generative AI and Large Language Models (LLMs).
• Previous startup experience.

• Opportunity to work on cutting-edge AI projects with a diverse range of clients and industries, driving solutions from development to production.
• Collaborative and supportive work environment, where you can grow and learn from a team of talented professionals.
• An opportunity to participate in conferences and workshops.
• An opportunity to participate in Tech Talks (internal training and seminar sessions).
• Remote work options and travel to European headquarters available.

• Medical package.
• Multisport cards.
• Lunch provided.
• Kitchens stocked with fruit and veggies twice a week.
• Monthly integration budget.
• Company library (online and offline).
• Fun room.

Join to apply for the Machine Learning Engineer role at Chubb

Get AI-powered advice on this job and more exclusive features.

As an MLOps Engineer, you’ll play a key role in operationalizing machine learning solutions that improve underwriting, claims, risk modeling, and customer experience. You will work closely with data scientists, data engineers, and actuarial teams to ensure ML models are production-ready, scalable, and resilient.

In this role, you will be responsible for building and maintaining ML pipelines using Databricks, PySpark, and Spark, automating the model lifecycle—from training to monitoring—on a robust cloud infrastructure.

• Design and implement automated ML pipelines for training, testing, deployment, and monitoring of models used in insurance applications such as claims prediction, fraud detection, and policy pricing.
• Build scalable data workflows using PySpark and Apache Spark within Databricks.
• Collaborate with data scientists and actuaries to package models and deliver reproducible, governed solutions.
• Implement CI/CD pipelines for ML using tools such as MLflow, Azure DevOps, or GitHub Actions.
• Develop and apply techniques for data drift and model drift detection, including statistical monitoring, performance baselines, and alerts.
• Set up monitoring, logging, and alerting frameworks to maintain ML model reliability in production.
• Ensure compliance with data privacy, regulatory standards, and model governance practices required in the insurance sector.

Desired Qualifications

• 3+ years of experience in MLOps, Data Science Engineering, or ML platform roles.
• Strong programming in Python, with solid expertise in PySpark.
• Deep knowledge of Spark and Databricks for big data processing and scalable ML.
• Proficient in SQL with the ability to work on complex joins and performance tuning.
• Experience operationalizing ML models in production (batch and real-time).
• Working knowledge of MLflow, Docker, Kubernetes, and cloud-native services (preferably Azure).
• Proven experience in implementing and managing data drift and model drift detection using statistical and ML-based methods.
• Familiar with insurance data domains (e.g., claims, underwriting, loss ratio, customer churn).
• Understanding of data governance, model risk management, and compliance in regulated industries.

Nice to Have

• Experience with Delta Lake, Unity Catalog, or Feature Stores.
• Knowledge of data mesh, event-driven architectures, or real-time streaming.
• Familiarity with actuarial modeling, telematics, or fraud analytics.
• Certifications in Azure, Databricks, or MLOps tools.

• Mid-Senior level

• Full-time

• Engineering and Information Technology

• Insurance

Join to apply for the Machine Learning Engineer role at Chubb

Get AI-powered advice on this job and more exclusive features.

As an MLOps Engineer, you’ll play a key role in operationalizing machine learning solutions that improve underwriting, claims, risk modeling, and customer experience. You will work closely with data scientists, data engineers, and actuarial teams to ensure ML models are production-ready, scalable, and resilient.

In this role, you will be responsible for building and maintaining ML pipelines using Databricks, PySpark, and Spark, automating the model lifecycle—from training to monitoring—on a robust cloud infrastructure.

• Design and implement automated ML pipelines for training, testing, deployment, and monitoring of models used in insurance applications such as claims prediction, fraud detection, and policy pricing.
• Build scalable data workflows using PySpark and Apache Spark within Databricks.
• Collaborate with data scientists and actuaries to package models and deliver reproducible, governed solutions.
• Implement CI/CD pipelines for ML using tools such as MLflow, Azure DevOps, or GitHub Actions.
• Develop and apply techniques for data drift and model drift detection, including statistical monitoring, performance baselines, and alerts.
• Set up monitoring, logging, and alerting frameworks to maintain ML model reliability in production.
• Ensure compliance with data privacy, regulatory standards, and model governance practices required in the insurance sector.

Desired Qualifications

• 3+ years of experience in MLOps, Data Science Engineering, or ML platform roles.
• Strong programming in Python, with solid expertise in PySpark.
• Deep knowledge of Spark and Databricks for big data processing and scalable ML.
• Proficient in SQL with the ability to work on complex joins and performance tuning.
• Experience operationalizing ML models in production (batch and real-time).
• Working knowledge of MLflow, Docker, Kubernetes, and cloud-native services (preferably Azure).
• Proven experience in implementing and managing data drift and model drift detection using statistical and ML-based methods.
• Familiar with insurance data domains (e.g., claims, underwriting, loss ratio, customer churn).
• Understanding of data governance, model risk management, and compliance in regulated industries.

Nice to Have

• Experience with Delta Lake, Unity Catalog, or Feature Stores.
• Knowledge of data mesh, event-driven architectures, or real-time streaming.
• Familiarity with actuarial modeling, telematics, or fraud analytics.
• Certifications in Azure, Databricks, or MLOps tools.

• Mid-Senior level

• Full-time

• Engineering and Information Technology

• Insurance

Sobre Coderio

Coderio diseña y entrega soluciones digitales escalables para empresas globales. Con una base técnica sólida y una mentalidad orientada al producto, nuestros equipos lideran proyectos de software complejos desde la arquitectura hasta la ejecución.

Valoramos la autonomía, la comunicación clara y la excelencia técnica. Colaboramos estrechamente con equipos y socios internacionales, construyendo tecnología que genera impacto.

Más información:

En este rol, liderarás el diseño, desarrollo e implementación de pipelines de machine learning en entornos escalables y de alto rendimiento, integrando tecnologías como Spark, TensorFlow y procesamiento en tiempo real con Kafka. Trabajarás junto a equipos multidisciplinarios, combinando ciencia de datos, sistemas distribuidos y visualización avanzada para generar insights clave que impacten decisiones estratégicas. Tendrás autonomía para proponer soluciones innovadoras y escalar modelos en entornos cloud (Azure).

Lo que puedes esperar de este rol (Responsabilidades)

Diseñar, construir y escalar pipelines de machine learning de alto rendimiento.

Integrar y procesar flujos de datos en tiempo real (ej. Kafka) en soluciones distribuidas.

Aplicar modelos de ML utilizando Python, Spark, TensorFlow y desplegarlos con prácticas de MLOps.

Visualizar y comunicar insights clave mediante herramientas como Power BI o Tableau.

Colaborar con equipos técnicos y de negocio en proyectos estratégicos de innovación.

Requisitos

+3 años de experiencia en ciencia o ingeniería de datos

Dominio de Python, SQL y NoSQL

Experiencia con Spark, TensorFlow y Kafka

Conocimiento en infraestructura cloud (Azure)

Inglés intermedio (documentación y colaboración técnica)

Deseable

Experiencia con MLflow y prácticas de MLOps

Conocimientos en modelado estadístico y series temporales

Visualización de datos con herramientas como Power BI o Looker

Uso de Docker y Kubernetes

Enfoque práctico orientado al negocio y a resultados

Beneficios

Compromiso a largo plazo, con autonomía e impacto

Rol estratégico y de alta visibilidad en una cultura de ingeniería moderna

Equipo internacional colaborativo y liderazgo técnico sólido

Plan de carrera crecimiento y liderazgo dentro de Coderio

¿Por qué unirte a Coderio?

En Coderio valoramos el talento sin importar la ubicación. Somos una empresa remote-first, apasionada por la tecnología, el trabajo colaborativo y la compensación justa. Ofrecemos un entorno inclusivo, desafiante y con oportunidades reales de crecimiento.

Si te motiva construir soluciones con impacto, te estamos esperando. Postula ahora.