201 – 500 Angajați
Sponsor pentru viza de lucrător calificatConsultanțăInteligență artificialăMarketingParser este o companie de soluții tehnologice și consultanță, fondată în 2018, care ajută organizațiile să aplice inteligența artificială, datele și ingineria software pentru nevoi de afaceri complexe. Activitatea sa include dezvoltare software personalizată, utilizarea inteligenței artificiale și a datelor, design de produs și consultanță pentru transformare digitală. Parser colaborează cu lideri din domeniul tehnologiei pentru a crea soluții adaptabile, concepute în funcție de obiectivele lor, combinând expertiza inginerească cu accentul pe inovația practică și pe parteneriate durabile cu clienții.
Senior Backend Engineer, MLOps
Build scalable MLOps infrastructure for a global travel industry leader, including data pipelines, model deployment, and real-time machine learning systems. Join Parser’s hybrid team in Spain.
Descriere
- Develop scalable infrastructure for machine learning training, deployment, and batch or real-time serving.
- Build and maintain data pipelines for large-scale processing and feature engineering.
- Tune compute usage and improve model-serving performance across ML systems.
- Set up monitoring, logging, and alerting for ML systems.
- Help develop ML operations practices, including CI/CD pipelines.
- Partner with data scientists to move models from development into production.
- Evaluate and integrate new technologies.
- Guide junior engineers through mentorship.
- Explain technical solutions to varied stakeholders.
Cerințe
- Bachelor’s or master’s degree in computer science, software engineering, or a related technical field.
- Typically 6–7 years of experience in software, data, or machine learning engineering roles.
- Strong problem-solving ability.
- Proficiency in Java; Python knowledge is desirable.
- Understanding of machine learning algorithms and model architectures.
- Experience developing scalable, reliable machine learning systems.
- Exposure to cloud platforms such as AWS.
- Experience with Docker or other containerization.
- Experience with scalable data systems such as Spark or Kafka.
- Familiarity with CI/CD tools such as GitHub Actions.
- Familiarity with model-serving technologies such as MLflow.
- Ability to work effectively across teams.
Beneficii
- Friendly, collaborative team that values knowledge sharing and ongoing learning.
- Action-oriented working culture.
- Opportunity to build MLOps and machine learning infrastructure for real-time systems using technologies such as Java, Python, AWS, Docker, Kafka, Spark, and MLflow.