MLOps Engineer - Production ML Pipelines & Deployments

TRANS.EU; Wrocław; On-site; PLN 150,000 - 280,000; Full time

Benefits offered by this job

LuxMed healthcare

Remote workstation budget

Gift vouchers

Buffet breakfasts

Team trips to resort

Multisport card

Job summary

TRANS.EU is seeking an ML Operations Engineer to build and maintain the infrastructure enabling ML models to run smoothly in production. You will work across GPUs, cloud, and on‑prem environments to tailor resources for teams, while designing CI/CD pipelines and model management tooling.

You will manage model registries, experiment tracking, and artifacts, deploy models to production, and build self‑service tools to accelerate deployment.

Qualifications

• Hands-on experience with CI/CD for ML models deployed in production.
• Ability to explain complex technical concepts simply.
• Proven collaboration with data science and ML teams.
• Experience with ML experimentation, model registries, and feature versioning.
• Familiarity with Snowflake for data retrieval and cost control.

Responsibilities

• Maintain and improve ML model training and deployment infrastructure.
• Design and implement ML CI/CD pipelines.
• Manage model registries, experiments, and artifact/versioning.
• Deploy models to production and build self-service tooling.
• Monitor production models for drift and performance; implement alerts.
• Develop MLOps standards and support GenAI/LLM initiatives.

Skills

CI/CD for ML models

Explain complex concepts clearly

Collaborate across teams

Polish language (B1+)

English language (B1+)

Experiment tracking

Model registries

Feature versioning

Communication

Data/ML infrastructure tooling

Tools

Python

SQL

Docker

Kubernetes/EKS

AWS (S3)

MLflow

Dagster

Airflow

Kubeflow

LiteLLM

Snowflake

Git

Terraform

Job description

TRANS.EU is seeking an ML Operations Engineer to build and maintain the infrastructure enabling ML models to run smoothly in production. You will work across GPUs, cloud, and on‑prem environments to tailor resources for teams, while designing CI/CD pipelines and model management tooling.

You will manage model registries, experiment tracking, and artifacts, deploy models to production, and build self‑service tools to accelerate deployment.