Remote MLOps Cloud Engineer (AWS) - ML Pipeline Expert

Andersen Lab; Warszawa; Hybrid; PLN 200,000 - 280,000; Full time

Benefits offered by this job

Private health insurance

Sports compensation

Referral program

Corporate training portal

Job summary

Andersen Lab is hiring an MLOps / Cloud Engineer (AWS) to design and operate an ML-powered image analysis platform for automated defect detection in industrial quality control. You will build the AWS-based ML pipeline, containerize services, and implement IaC and CI/CD practices to enable robust, scalable deployments.

The role requires 5+ years in DevOps/Cloud with AWS, hands-on ML/Python deployment on AWS (SageMaker), and experience with MLOps tooling.

Qualifications

• 5+ years of experience in DevOps/Cloud with AWS as the primary platform.
• Hands-on experience deploying ML models or Python services on AWS (SageMaker, endpoints, Step Functions).
• Experience with MLOps tooling (MLflow or similar).
• Terraform in real‑world projects.
• CI/CD pipelines with GitLab CI (GitHub Actions acceptable).
• Docker and container deployments on AWS (ECS/EKS/Lambda).
• Experience with S3 and RDS in production environments.
• Managing multi‑environment infra (dev, pre-prod, prod).
• English level: Intermediate+ or higher.

Responsibilities

• Design and set up AWS architecture for the ML pipeline (ingestion, inference, storage).
• Build CI/CD pipelines in GitLab for application and model components.
• Implement Infrastructure as Code with Terraform.
• Set up multi-environment deployment targets (dev/pre-prod/prod).
• Automate provisioning and teardown of environments.
• Containerize services and integrate AWS storage/services.
• Set up pipeline monitoring, logging, and traceability.
• Document infrastructure and deployment for knowledge transfer.

Skills

AWS

DevOps

MLOps

Terraform

GitLab CI

Docker

SageMaker

ECS/EKS/Lambda

S3/RDS

English Proficiency

Tools

Terraform

GitLab CI

Docker

SageMaker

Job description

Andersen Lab is hiring an MLOps / Cloud Engineer (AWS) to design and operate an ML-powered image analysis platform for automated defect detection in industrial quality control. You will build the AWS-based ML pipeline, containerize services, and implement IaC and CI/CD practices to enable robust, scalable deployments.

The role requires 5+ years in DevOps/Cloud with AWS, hands-on ML/Python deployment on AWS (SageMaker), and experience with MLOps tooling.