Parser
Parser
Parser एक प्रौद्योगिकी समाधान और परामर्श कंपनी है, जिसकी स्थापना 2018 में हुई थी। यह संगठनों को जटिल व्यावसायिक आवश्यकताओं के लिए कृत्रिम बुद्धिमत्ता, डेटा और सॉफ्टवेयर इंजीनियरिंग का उपयोग करने में सहायता करती है। इसका कार्य कस्टम सॉफ्टवेयर विकास, AI और डेटा के उपयोग, उत्पाद डिजाइन तथा डिजिटल रूपांतरण परामर्श तक फैला हुआ है। Parser तकनीकी क्षेत्र के नेताओं के साथ मिलकर उनके लक्ष्यों के अनुरूप अनुकूलनीय समाधान तैयार करती है और इंजीनियरिंग विशेषज्ञता को व्यावहारिक नवाचार तथा दीर्घकालिक ग्राहक साझेदारियों पर ध्यान देने के साथ जोड़ती है।

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.

विवरण

  • 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.

आवश्यकताएँ

  • 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.

लाभ

  • 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.

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