We are seeking for a Machine Learning Engineer with strong expertise in MLOps and Data Engineering to join our team. This role involves working closely with Data Scientists and MLOps specialists to design implement and maintain machine learning systems and pipelines in a production environment. You will be responsible for supporting end-to-end ML workflows ensuring scalability reliability and efficiency in deployed models.
The ideal candidate will have a solid understanding of the machine learning lifecycle experience in AWS cloud services (particularly SageMaker) and the ability to build and maintain CI/CD pipelines for ML workloads.
What is this position about
- Collaborate with Data Scientists to operationalize ML models and integrate them into production systems.
- Design build and maintain MLOps pipelines for model deployment monitoring and retraining.
- Develop and manage ETL workflows and data pipelines to support ML processes.
- Implement infrastructure as code solutions (preferably AWS CloudFormation or Terraform).
- Work with AWS services such as SageMaker Lambda Step Functions DynamoDB and others.
- Use Docker for containerization and manage cloud-based deployments.
- Perform queries on Snowflake as needed for data analysis and validation.
- Ensure best practices for scalability performance and security in ML systems.
- Take a leadership role in guiding technical decisions and mentoring when required.
Qualifications :
- Degree in Computer Science Engineering or a related field or equivalent experience.
- Strong proficiency in Python and PySpark.
- Hands-on experience with AWS cloud services.
- Experience with SageMaker.
- Solid understanding of infrastructure as code principles (CloudFormation preferred).
- Knowladge of MLOps concepts and the machine learning lifecycle
- Experience building and maintaining CI/CD pipelines.
- Experience with ETL processes.
- Familiarity with Docker for containerization.
- Basic working knowledge of Snowflake (a plus).
- Demonstrated leadership skills including guiding technical work and defining tasks.
- Excellent problem-solving skills with the ability to work independently
- Strong collaboration and communication skills comfortable working in global multicultural teams.
- Highly proactive self-motivated and able to work with minimal supervision.
What about languages
You will need excellent written and verbal English for clear and effective communication with the team.
How much experience must I have
In order to thrive in this role you must have at least 5 years of experience in similar roles.
Additional Information :
Our Perks and Benefits:
Learning Opportunities:
Travel opportunities to attend industry conferences and meet clients.
Mentoring and Development:
Celebrations & Support:
Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location please consult with one of our recruiters.
Remote Work :
No
Employment Type :
Full-time
We are seeking for a Machine Learning Engineer with strong expertise in MLOps and Data Engineering to join our team. This role involves working closely with Data Scientists and MLOps specialists to design implement and maintain machine learning systems and pipelines in a production environment. You ...
We are seeking for a Machine Learning Engineer with strong expertise in MLOps and Data Engineering to join our team. This role involves working closely with Data Scientists and MLOps specialists to design implement and maintain machine learning systems and pipelines in a production environment. You will be responsible for supporting end-to-end ML workflows ensuring scalability reliability and efficiency in deployed models.
The ideal candidate will have a solid understanding of the machine learning lifecycle experience in AWS cloud services (particularly SageMaker) and the ability to build and maintain CI/CD pipelines for ML workloads.
What is this position about
- Collaborate with Data Scientists to operationalize ML models and integrate them into production systems.
- Design build and maintain MLOps pipelines for model deployment monitoring and retraining.
- Develop and manage ETL workflows and data pipelines to support ML processes.
- Implement infrastructure as code solutions (preferably AWS CloudFormation or Terraform).
- Work with AWS services such as SageMaker Lambda Step Functions DynamoDB and others.
- Use Docker for containerization and manage cloud-based deployments.
- Perform queries on Snowflake as needed for data analysis and validation.
- Ensure best practices for scalability performance and security in ML systems.
- Take a leadership role in guiding technical decisions and mentoring when required.
Qualifications :
- Degree in Computer Science Engineering or a related field or equivalent experience.
- Strong proficiency in Python and PySpark.
- Hands-on experience with AWS cloud services.
- Experience with SageMaker.
- Solid understanding of infrastructure as code principles (CloudFormation preferred).
- Knowladge of MLOps concepts and the machine learning lifecycle
- Experience building and maintaining CI/CD pipelines.
- Experience with ETL processes.
- Familiarity with Docker for containerization.
- Basic working knowledge of Snowflake (a plus).
- Demonstrated leadership skills including guiding technical work and defining tasks.
- Excellent problem-solving skills with the ability to work independently
- Strong collaboration and communication skills comfortable working in global multicultural teams.
- Highly proactive self-motivated and able to work with minimal supervision.
What about languages
You will need excellent written and verbal English for clear and effective communication with the team.
How much experience must I have
In order to thrive in this role you must have at least 5 years of experience in similar roles.
Additional Information :
Our Perks and Benefits:
Learning Opportunities:
Travel opportunities to attend industry conferences and meet clients.
Mentoring and Development:
Celebrations & Support:
Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location please consult with one of our recruiters.
Remote Work :
No
Employment Type :
Full-time
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