Machine Learning Engineer (Production)

Zohorecruit; Polska; Hybrid; PLN 210,000 - 310,000; Full time

Job summary

Zohorecruit is seeking a highly experienced Machine Learning Engineer for a remote role. You will design, deploy, and maintain production-ready ML systems, taking models from experimentation to scalable, monitored environments.

Colaboration with Data Scientists, Data Engineers, and Platform teams is key to operationalizing ML models, building robust data pipelines, and implementing MLOps best practices.

Qualifications

• 4+ years in Machine Learning Engineering or Applied ML.
• Strong Python programming skills.
• Experience deploying models to production (APIs, batch, or streaming).
• Experience with Docker and containerized environments.
• Kubernetes experience for scaling ML services.
• Familiarity with ML Ops tools (MLflow, model registries).
• Solid preprocessing and feature engineering knowledge.
• Experience with AWS, Azure, or GCP.

Responsibilities

• Design and implement scalable ML systems for real-time and batch inference.
• Build deployment pipelines using containers and CI/CD.
• Develop APIs and services for serving ML models.
• Monitor model performance, drift, and data quality.
• Collaborate with Data Engineers on feature pipelines.
• Manage model versioning, reproducibility, and governance.
• Optimize inference performance and cloud costs.
• Support retraining workflows and continuous improvement.
• Ensure security and governance for data and models.

Skills

Machine Learning

Continuous Improvement

+18

Tools

Docker

Kubernetes

MLflow

CI/CD

Job description

• Required Skills
  • machine learning
  • continuous improvement
  • +18
• Remote Job

Job Description

This is a remote position.

We are seeking a highly experienced Machine Learning Engineer to design, deploy, and maintain production-ready machine learning systems. This role focuses on taking models from experimentation to scalable, monitored, and secure production environments.

You will collaborate with Data Scientists, Data Engineers, and Platform teams to operationalize ML models, build robust data pipelines, and implement MLOps best practices. The ideal candidate understands the full ML lifecycle, including model training, validation, deployment, monitoring, retraining, and governance.

This is not a research-only role. We are looking for engineers who have deployed models into real-world production systems.

Key Responsibilities:

• Design and implement scalable ML systems for real-time and batch inference
• Build model deployment pipelines using containerization and CI/CD
• Develop APIs and services for serving machine learning models
• Implement monitoring and alerting for model performance, drift, and data quality
• Collaborate with Data Engineers to ensure reliable feature pipelines
• Manage model versioning, reproducibility, and governance
• Optimize inference performance and cloud cost efficiency
• Support retraining workflows and continuous improvement
• Ensure security and compliance standards for data and models

Requirements

Requirements

• 4+ years of experience in Machine Learning Engineering or Applied ML
• Strong programming skills in Python
• Hands‑on experience with PyTorch, TensorFlow, or similar frameworks
• Experience deploying models into production (API‑based, batch, or streaming)
• Experience with Docker and containerized environments
• Familiarity with Kubernetes for scaling ML services
• Experience with MLOps tools (MLflow, model registry, CI/CD integration)
• Strong understanding of feature engineering and data preprocessing
• Experience working in AWS, Azure, or GCP environments
• Knowledge of monitoring, logging, and observability tools

Advanced / Preferred Qualifications

• Experience with distributed training or large‑scale data processing
• Experience with feature stores or vector databases
• Experience deploying LLM‑powered applications or RAG systems
• Experience implementing model drift detection and automated retraining
• Understanding of security, IAM, and data governance for ML systems
• Experience in high‑availability production environments

Ideal Candidate Profile

The ideal candidate:

• Has moved models from notebook to production
• Understands both ML and software engineering principles
• Has worked on systems serving real users or business‑critical workflows
• Thinks about reliability, cost, and scalability