AI Engineer / Machine Learning Engineer – MLOps

KATBOTZ LLC; Poland; Hybrid; PLN 180,000 - 360,000; Full time

Job summary

KATBOTZ LLC is seeking an AI Engineer with strong MLOps experience to design, deploy, monitor, and maintain ML models in production. You will build scalable ML pipelines, automate deployment, and ensure reliability, governance, and performance across cloud platforms.

The role requires expertise in Python, MLOps tools, Docker, CI/CD, MLflow/Kubeflow/Airflow, APIs, and experience deploying models to production. Remote-friendly with flexible working conditions.

Qualifications

• 3–7 years of experience in Machine Learning / AI / Data Engineering.
• 2+ years in MLOps / Model Deployment / ML Pipelines.
• Experience deploying models to production is mandatory.

Responsibilities

• Build and maintain ML pipelines for training, testing, and deployment.
• Deploy machine learning and AI models into production.
• Automate workflows using CI/CD for ML models.
• Work with data scientists and AI developers to productionize models.
• Manage model versioning, data versioning, and experiment tracking.
• Deploy models on cloud platforms (AWS, Azure, GCP).
• Containerize applications using Docker and Kubernetes.
• Implement monitoring and logging for ML systems.

Skills

Python

MLOps tools & frameworks

Docker

CI/CD

MLflow Kubeflow Airflow

APIs

SQL NoSQL databases

Model monitoring & logging

Tools

MLflow

Airflow

DVC

Weights & Biases

SageMaker

Docker

Terraform

Job description

We are looking for an AI Engineer with strong experience in Machine Learning Operations (MLOps) to design, deploy, monitor, and maintain machine learning and AI models in production environments. The candidate will be responsible for building scalable ML pipelines, automating model deployment, managing model lifecycle, and ensuring reliability, performance, and governance of AI systems.

Key Responsibilities

• Build and maintain ML pipelines for training, testing, and deployment
  
• Deploy machine learning and AI models into production environments
  
• Automate workflows using CI/CD for ML models
  
• Work with data scientists and AI developers to productionize models
  
• Manage model versioning, data versioning, and experiment tracking
  
• Deploy models on cloud platforms (AWS, Azure, GCP)
  
• Containerize applications using Docker and Kubernetes
  
• Implement monitoring and logging for ML systems
  
• Ensure scalability, security, and reliability of AI systems
  

Requirements

Required Skills

• Python
  
• MLOps tools and frameworks
  
• Docker
  
• CI/CD (GitHub Actions, Jenkins, GitLab CI)
  
• MLflow / Kubeflow / Airflow
  
• APIs (FastAPI / Flask)
  
• SQL / NoSQL databases
  
• Model monitoring and logging
  

MLOps Tools (Important)

Candidate should have experience in some of these:

• MLflow
  
• Airflow
  
• DVC
  
• Weights & Biases
  
• SageMaker
  
• Docker
  
• Terraform
  

Experience Required

• 3–7 years in Machine Learning / AI / Data Engineering
  
• 2+ years in MLOps / Model Deployment / ML Pipelines
  
• Experience deploying models to production is mandatory
  

Education

• Competitivecompensationpackage
  
• Opportunitiesforprofessionaldevelopmentandcareeradvancement.
  
• Flexibleworkingconditions,withremoteoptionsavailable.
  
• Dynamicandsupportiveworkenvironment.
  

KATBOTZLLCisanEqualOpportunityEmployer.Weprovideequalemploymentopportunitiestoallqualifiedindividuals,regardlessofrace,religion,gender,genderidentity,age,maritalstatus,nationalorigin,sexualorientation,citizenshipstatus,veteranstatus,disability,oranyotherlegallyprotectedstatus.Asanorganization,weareunwaveringinourcommitmenttomaintainingadiscrimination-freeworkenvironment,andfosteringacultureofinclusivity,belongingandequalopportunityforallemployeesandapplicants.