2 дня назад
MLOps Engineer Senior (Machine Learning)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
MLOps Engineer Senior (Machine Learning): Industrializing, deploying, monitoring, and scaling machine learning models in production with an accent on training and inference pipelines, model governance, and reliable ML platforms. Focus on building CI/CD workflows, exposing scalable model-serving APIs, orchestrating Databricks jobs, and deploying containerized applications on Kubernetes-based infrastructure.
Location: Remote - Latin America
Company
A remote startup building data products, machine learning systems, scalable data infrastructure, and custom AI solutions for e-commerce, retail, and other industries.
What you will do
- Industrialize, deploy, monitor, and scale machine learning models in production.
- Design and maintain end-to-end training, inference, and retraining pipelines.
- Build CI/CD pipelines for machine learning workflows and manage model tracking, versioning, and registries with MLflow.
- Develop scalable APIs for model serving and orchestrate workflows and jobs on Databricks.
- Containerize machine learning applications with Docker and support deployment on Kubernetes-based infrastructure.
- Collaborate with Data Scientists, Data Engineers, and business stakeholders to implement reliable ML architectures and promote MLOps best practices.
Requirements
- Advanced Python and SQL skills.
- Experience with Spark or PySpark, Git, and CI/CD pipelines.
- Experience with MLflow, including tracking, registry, and deployment.
- Experience with Docker and working knowledge of Kubernetes concepts.
- Experience with Azure Cloud, model monitoring, observability, and production-scale model deployment.
- Strong understanding of MLOps and machine learning architecture principles.
Nice to have
- Hands-on experience with Databricks Workflows, Jobs, and Repos.
- Experience with AWS or GCP.
- Experience using Kubernetes in production environments.
Culture & Benefits
- Remote-first work culture.
- In-company English lessons.
- Wellhub or sports club stipend.
- Company-funded AWS, dbt, Google Cloud, Azure, and Databricks certifications.
- Birthday day off and an additional vacation week.
- Food credits, referral bonuses, annual team trip, and monthly childcare reimbursement.
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