MLOps Engineer @ apreel Sp. z o.o.

Profil : MLOps Engineer

The opportunity :

We are looking for an experienced Senior MLOps Engineer to design, build, and optimize scalable machine learning infrastructure on AWS. In this role, you’ll work closely with data scientists and software engineers to productionize ML models, automate workflows, and ensure reliability, security, and observability across our ML ecosystem.

You’ll be responsible for shaping and maintaining the full ML lifecycle — from data pipelines and model training to deployment, monitoring, and continuous improvement.

Offer :

• Localization : Warszawa / Remote + equipment pick up from Warsaw / Wrocław
• Start : ASAP
• Employment : B2B contract with apreel
• Salary : up to 165 PLN / h+ VAT

To qualify for the role, you must have :

Profil : MLOps Engineer

The opportunity :

We are looking for an experienced Senior MLOps Engineer to design, build, and optimize scalable machine learning infrastructure on AWS. In this role, you’ll work closely with data scientists and software engineers to productionize ML models, automate workflows, and ensure reliability, security, and observability across our ML ecosystem.

You’ll be responsible for shaping and maintaining the full ML lifecycle — from data pipelines and model training to deployment, monitoring, and continuous improvement.

Offer :

,[Design, build, and maintain end-to-end ML pipelines using SageMaker Pipelines or Kubeflow. , Implement CI / CD workflows for ML using tools such as GitHub Actions, GitLab, Jenkins, or AWS CodePipeline. , Manage containerized deployments with Docker and Kubernetes (ECS / EKS). , Oversee model versioning, registry, and tracking with MLflow or SageMaker Model Registry. , Develop and operate monitoring solutions for model performance, drift detection, and data quality. , Build and maintain data engineering workflows using AWS Glue, EMR, Spark, or PySpark. , Implement Infrastructure as Code (IaC) using Terraform or AWS CloudFormation. , Ensure observability and reliability using CloudWatch, Prometheus, ELK, or Datadog. , Follow AWS security best practices (IAM, VPC, KMS, Secrets Manager, PrivateLink). , Collaborate with cross-functional teams to integrate ML systems into production applications. ] Requirements : MLOps, Machine learning, AWS, Security, Data pipelines, Kubeflow, GitHub Actions, GitLab, Jenkins, AWS CodePipeline, Docker, Kubernetes, ECS, AWS ECS, Amazon EKS, MLflow, Data engineering, AWS Glue, Amazon EMR, Spark, PySpark, Infrastructure as Code, Terraform, AWS Cloud, CloudWatch, Prometheus, ELK Stack, Datadog, IAM, VPC, KMS, AWS SageMaker, AWS Lambda, AWS S3, Python, PyTorch, TensorFlow, scikit-learn, Glue, Networking Tools : Jenkins, GitLab.