Sr. Machine Learning Engineer
Remote in Argentina / Remote in MexicoFull-TimeSenior
Salary70,000 - 87,000 USD per year
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Job Details
- Experience
- 5+ years of hands-on experience in ML Engineering, with a significant focus (3+ years) on the data engineering side of the house.
- Required Skills
- AWSDockerPythonSQLDynamoDBKafkaKubernetesPyTorchSnowflakeData engineeringTensorflowTerraformDatabricksCloudFormationscikit-learn
Requirements
- You have 5+ years of hands-on experience in ML Engineering, with a significant focus (3+ years) on the data engineering side of the house.
- You are an expert in the AWS ecosystem (Sagemaker, DynamoDB) and thrive using Infrastructure as Code tools like Terraform, CDK, or CloudFormation.
- You have a deep understanding of containerization and orchestration, specially using Docker and Kubernetes to deploy scalable workloads.
- You possess a deep understanding of database systems, ETL architecture, and advance SQL, alongside mastery of Python and frameworks like TensorFlow, PyTorch, or Scikit-learn.
- You excel at working across functional lines - Translating Data Science needs into engineering requirements and mentoring others on best practices.
- You ideally have experience with Snowflake, Databricks, or streaming technologies like Kafka to handle event-base data at scale.
Responsibilities
- Architect the ML Ecosystem: You will own the end-to-end lifecycle of our ML infrastructure, designing a scalable, modern environment that enables models to thrive in production.
- Productionize Innovation: Partner closely with our Data Science team to take complex algorithms from the "lab" to the "real-world", building the high-performance pipelines required to scale them.
- Engineer Feature Intelligence: Design and maintain both offline and online feature stores, ensuring our models have the high-quality data they need for instant decision-making.
- Escale the Data Platform: Collaborate with Data Engineers to evolve our data lake and ETL architectures, ensuring our data platform remains robust and future-proof.
- Ensure System Health: Lead the monitoring and observability strategy for all production models, ensuring reliability and performance through proactive maintenance.
- Shape Technical Strategy: Act as a key stakeholder in architectural decisions, helping the broader team define the strategy for our data products and event-driven architectures.
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