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We are partnering with a leading US-based data consultancy to find a talented MLOps Data Engineering Consultant to join their remote-first team. This is a full-time, fully remote role open to candidates located in Latin America , supporting a range of client engagements focused on deploying machine learning models into production.
About the Role:
Our client is looking for a consultant who excels at operationalizing ML workflows and building scalable data infrastructure. While prior experience designing and building ML models is a plus, the core focus is on deployment and engineering . You'll work closely with data scientists and engineers to ensure robust, cloud-native ML pipelines.
Key Responsibilities:
- Deploy and maintain ML models in production environments.
- Build and optimize data workflows using Python and SQL.
- Collaborate with cross-functional teams to support scalable ML operations.
- Work primarily within AWS and Snowflake environments.
- Utilize AWS SageMaker for model deployment and lifecycle management.
- Contribute to multiple client projects across industries and tech stacks.
Required Skills:
- Strong experience with Python and SQL .
- Hands-on expertise with AWS , especially SageMaker .
- Familiarity with Snowflake as a data platform.
- Solid understanding of MLOps principles and CI/CD for ML.
- Comfortable working independently in a remote consulting environment.
Nice to Have:
- Experience designing and building ML models.
- Exposure to other cloud platforms (e.G., GCP, Azure).
- Familiarity with Docker and container orchestration.
Why This Role?
- Join a dynamic consultancy with a strong reputation in data and AI.
- Work remotely with a global team of experts.
- Engage in diverse, high-impact projects using modern tech stacks.
