We are seeking an MLOps Engineer with a strong background in Python, cloud infrastructure, and experience with machine learning workflows. The ideal candidate will be adept at building, deploying, and maintaining ML pipelines, with a focus on scalability, reliability, and automation. Experience with tools such as Dagster or SageMaker is a bonus. You will play a crucial role in operationalizing models, ensuring that both data and models are production-ready, and building systems that enable the full machine learning lifecycle. 

No candidate will meet every single desired qualification.  If your experience looks a little different from what we identify below and you think you can bring value to the role, we’d love to hear from you!

Culturally, you're:

• A deeply curious engineer who thrives in a high-performing team environment
• Collaborative, a clear communicator, and effective in working with diverse stakeholders
• Someone who values team results and well-being, checks your ego at the door, and takes pride in owning outcomes
• Motivated by setting ambitious goals and achieving them as a team
• A person with a sense of humor and a willingness to grow in a friendly, fun environment

Technically, you:

• Pick up new technology and switch between tech stacks with a minimum of fuss 
• Are comfortable working on cloud-based infrastructure components in one or more of the major providers (AWS, GCP, Azure)
• Have worked on globally distributed agile teams to deliver software iteratively

At a minimum, you have: 

• 4+ years of experience in MLOps, Data engineering, or a related field
• Strong proficiency in common data tools (Python, dbt, Spark)
• 3+ years of experience with SQL and working with relational databases
• Experience deploying and maintaining ML models in production environments
• Experience with Snowflake or similar data warehousing solutions
• Experience in building and maintaining data pipelines
• Strong communication skills in English

It would be nice if you have:

• Experience with feature stores and model registries
• Experience with monitoring and observability of ML systems (drift detection, logging, alerting)
• Familiarity with data engineering workflows and ETL processes (Dagster, Airflow)
• Experience with cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)

This role is open to candidates across ALL of LATAM.

Want to learn more about InTune and our team? Check out our YouTube - https://www.youtube.com/@intuneanalytics