10 days ago
Remote, United StatesSenior
Responsibilities
- Industrialize, deploy, and scale machine learning models in production environments.
- Design and maintain end-to-end training, inference, and retraining pipelines.
- Build and maintain CI/CD pipelines for machine learning workflows and manage MLflow tracking, versioning, registries, and deployments.
- Develop and expose scalable model-serving APIs.
- Orchestrate workflows and jobs in Databricks, including Workflows, Jobs, and Repos.
- Containerize ML applications with Docker and support deployment on Kubernetes-based infrastructure.
- Implement model governance, monitoring, observability, and lifecycle traceability practices.
- Collaborate with Data Scientists, Data Engineers, and business stakeholders and promote MLOps and modern ML architecture practices.
Requirements
- Advanced Python and SQL skills.
- Experience with Spark or PySpark.
- Experience with CI/CD pipelines and Git.
- Experience with MLflow tracking, registry, and deployment.
- Experience with Docker and working knowledge of Kubernetes concepts.
- Experience with Azure Cloud.
- Experience implementing model monitoring and observability practices.
- Strong understanding of MLOps and ML architecture principles.
- Experience deploying machine learning models to production at scale.
- Preferred: hands-on Databricks experience, including Workflows, Jobs, and Repos.
- Preferred: experience with AWS, GCP, and Kubernetes in production environments.
Benefits
- Remote-first culture with work-from-anywhere flexibility.
- In-company English lessons.
- Wellhub or sports club stipend.
- AWS, dbt, Google Cloud, Azure, and Databricks certifications fully covered.
- Food credits through Pedidos Ya.
- Birthday off and an extra vacation week.
- Referral bonuses.
- Annual team trip.
- Monthly childcare reimbursement.
Tech Stack
Apache SparkAWSAzureDatabricksdbtDockerGitGoogle CloudGoogle Cloud PlatformKubernetesMLflowPythonSQL
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