ML Engineer

TL;DR

ML Engineer (MLOps/Production ML): Supporting and maintaining production machine learning capabilities for demand forecasting with an accent on reliability, scalability, and explainability. Focus on monitoring and troubleshooting ML workflows, automating MLOps pipelines, and building feature engineering and inference monitoring systems.

Location: Warsaw Data Science

Company

hirify.global builds responsible AI for accurate, granular demand predictions and real-time decision intelligence for commercial teams.

What you will do

• Collaborate with cross-functional teams to keep ML systems robust, explainable, and aligned with business needs.
• Monitor and report ML model performance, reliability, and explainability metrics; extend monitoring pipelines for new features.
• Participate in model retraining and implement automation/optimization of MLOps pipelines.
• Investigate and resolve issues in production ML workflows from triage to root-cause analysis with model owners.
• Develop and maintain repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools.
• Build and maintain static and temporal features (seasonality, event-based, price-related) and run EDA to maintain data health.

Requirements

• 5+ years of hands-on experience in data science, ML operations, or applied ML support.
• Proficiency in Python and standard data/ML libraries (Pandas/Polars, NumPy, Scikit-learn, SQL); PyTorch or TensorFlow experience is a plus.
• Strong skills in data visualization and exploratory data analysis for monitoring and debugging pipelines.
• Experience with time-series data and feature engineering.
• Familiarity with explainability tools and model monitoring best practices.
• Experience with cloud-based ML platforms, preferably GCP; familiarity with Docker/Kubernetes, CI/CD, and ML observability tools.

Nice to have

• Experience with orchestration tools such as Airflow, Kedro, or Dagster.
• Prior exposure to demand forecasting, pricing, or revenue management.
• Experience with production based adjusters across customer deployments.

Culture & Benefits

• Work closely with production systems to ensure ML reliability, scalability, and explainability.
• Collaborate with research teams to help deliver impact faster in a fast-moving environment.
• Focus on transparent, glass-box AI architecture and measurable business outcomes.