Salve.Inno ir 2024. gadā dibināts personāla atlases un konsultāciju uzņēmums, kura galvenais birojs atrodas Gdaņskā, Polijā. Tā nelielā komanda palīdz uzņēmumiem pieņemt darbā darbiniekus, izmantojot pielāgotas personāla atlases stratēģijas, globālu attālināto darbinieku meklēšanu, kandidātu atlasi un karjeras vietņu risinājumus. Uzņēmums darbojas konsultāciju, mārketinga un loģistikas jomā, bet tā personāla atlases pieredze aptver mārketinga, forex un iGaming amatus. Salve.Inno atbalsta organizācijas arī komunikācijas, daudzveidības, vienlīdzības un iekļaušanas, kā arī efektīvāku personāla atlases procesu izstrādes jomā, publicē attālināta darba iespējas un sniedz konsultāciju pakalpojumus globālajam talantu tirgum.
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.
Apraksts
- 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.
Prasības
- 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.
Priekšrocības
- 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.