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Salve.Inno
Salve.Inno
11 – 50 Angajați
ConsultanțăLogisticăMarketing
Salve.Inno este o companie de recrutare și consultanță fondată în 2024, cu sediul în Gdańsk, Polonia. Echipa sa restrânsă ajută companiile să angajeze prin strategii de recrutare personalizate, identificarea globală a candidaților pentru roluri la distanță, selectarea candidaților și experiențe de recrutare pentru site-urile de carieră. Compania activează în domeniile consultanței, marketingului și logisticii, având experiență în recrutarea pentru roluri din marketing, forex și iGaming. Salve.Inno sprijină, de asemenea, organizațiile în domeniul comunicării, al diversității, echității și incluziunii, precum și în dezvoltarea unor procese de recrutare mai eficiente, publicând în același timp oportunități de lucru la distanță și oferind servicii de consultanță pentru o piață globală a talentelor.
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Locurile de muncă ale companiei

Senior Machine Learning Engineer, Applied AI and MLOps Lead

Lead production AI solutions and MLOps for an international organisation. Shape predictive models, governance, deployment, and scalable machine learning architecture.

PLPoloniaHibridNormă întreagăSeniorLimbă Obligatorie: englezăInginer de învățare automată
ApachePySparkPythonSparkSQL

Descriere

  • Own machine learning models from development through production monitoring and retraining.
  • Lead applied AI projects in customer lifetime value, churn, fraud, and abuse prevention.
  • Build and improve MLOps frameworks, automated deployments, CI/CD, environment promotion, and model lifecycle management.
  • Create monitoring for model performance, data drift, data quality, and business outcomes.
  • Set model retraining strategies, escalation procedures, and continuous improvement practices.
  • Use SHAP and feature attribution to make models more transparent and interpretable.
  • Set standards for model governance, documentation, version control, reproducibility, and auditability.
  • Partner with Data Engineering on scalable pipelines, feature engineering, and dependable data infrastructure.
  • Turn business challenges into practical, scalable AI solutions with Product, Risk, Commercial, and other teams.
  • Review production model performance to improve accuracy, reliability, and business impact.
  • Maintain production standards for stability, scalability, maintainability, and operational efficiency.
  • Guide and mentor fellow engineers.
  • Balance fast MVP delivery with lasting production reliability and scalability.

Cerințe

  • Bring 5–8 or more years of professional experience building, deploying, and maintaining machine learning models in production.
  • Have advanced Python skills and strong software engineering foundations.
  • Understand machine learning algorithms, model evaluation, feature engineering, and predictive modelling.
  • Have practical experience with production ML issues such as data leakage, model drift, data quality, and model stability.
  • Have hands-on experience with Apache Spark or PySpark for distributed computing and large-scale data processing.
  • Have used MLflow or a comparable platform for managing the machine learning lifecycle.
  • Have designed, implemented, and maintained CI/CD pipelines, preferably with GitHub Actions.
  • Have advanced SQL skills and experience working with large, complex datasets.
  • Understand model deployment, monitoring, drift detection, retraining, and production maintenance.
  • Have implemented model explainability and interpretability methods such as SHAP.
  • Have delivered AI/ML solutions that produced measurable commercial or operational improvements.
  • Understand model governance, versioning, documentation, and reproducibility requirements.
  • Be able to design scalable, reliable, and maintainable machine learning architectures.
  • Bring strong analytical thinking and problem-solving skills.
  • Communicate complex technical ideas clearly to non-technical stakeholders.
  • Have worked across teams such as Data Engineering, Product, Risk, and Commercial.
  • Balance fast development and MVP delivery with sound production engineering.
  • Show leadership through mentoring, technical influence, and engineering best practices.
  • Take a proactive, independent, results-oriented approach to delivering AI solutions.

Beneficii

  • Competitive compensation based on experience and qualifications.
  • Comprehensive employee training and professional development programmes.
  • Opportunities to lead high-impact AI and machine learning initiatives.
  • Exposure to advanced machine learning technologies and modern MLOps practices.
  • An international workplace with cross-functional collaboration.
  • Company, team, and group events.
  • Birthday and loyalty benefits.
  • Opportunities for professional growth and technical leadership.
  • Additional employee benefits and incentives.

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