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🔥 Data Engineer, Cyber
Posted 2024-11-12

📍 Quebec

🔍 Cybersecurity

  • 4+ years of experience in data warehousing and ETL development.
  • 3+ years of experience with Apache Spark or Databricks.
  • 2+ years of experience building, deploying, and optimizing data-ingestion solutions.
  • Experience working directly with external and internal stakeholders with good communication skills.
  • A bachelor's or master's degree in computer science or engineering or equivalent professional experience.

  • Analyze, design, and implement features.
  • Write well-designed and efficient code, including unit and integrated tests.
  • Correct anomalies and problems as they arise.
  • Test features to ensure acceptance criteria are met.
  • Participate in projects from initial idea to launch.
  • Advise the product owner on technological choices and development decisions.
  • Contribute to continuous improvement in development activities such as agility and automated testing.
  • Collaborate with the security team to integrate security best practices into software processes.
  • Communicate risks related to activities, technologies, or processes as they are identified.

Software DevelopmentETLSparkCommunication SkillsTime ManagementMicroservices

Posted 2024-11-12
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📍 Quebec, Ontario

🔍 Cybersecurity

  • 5+ years of experience with the following ML model techniques: LLMs, Deep Learning, traditional ML and predictive modeling.
  • 5+ years of experience with Python programming.
  • 3+ years experience with building, validating, deploying and monitoring production models.
  • Strong skills in using generative AI to speed up features delivery.
  • Strong understanding of Generative AI techniques and models.
  • Experience with ML frameworks such as Tensorflow, PyTorch.
  • Experience with infrastructure and tooling for MLOps.
  • Excellent communication and collaboration skills.

  • Package LLM models, incorporating Generative AI, deploy to production environments, and monitor usage and performance.
  • Collaborate with engineers and product managers to integrate Generative AI features into existing systems.
  • Experiment with different Generative AI techniques and architectures to optimize both delivery and performance.
  • Evaluate and validate the effectiveness of Generative AI models using appropriate metrics.
  • Manage ML infrastructure in the cloud.
  • Test models at scale and ensure CI/CD best practices are followed by the whole team.
  • Diagnose and resolve ML workflow and production issues quickly.
  • Design, develop, and maintain machine learning algorithms that work on machines with limited resources as well as in cloud environments.
  • Create and maintain scalable feature pipelines.
  • Integrate the algorithms in the current systems, in collaboration with the engineering teams.
  • Write production-level code to convert ML models into working pipelines.
  • Correct anomalies and problems as they arise.
  • Test the implemented features to make sure all acceptance criteria are met.
  • Participate in projects from the initial idea to launch.
  • Contribute to the continuous improvement of development activities (agility, automated tests, deployment, etc.).
  • Collaborate with the security team to integrate security best practices into software development processes.
  • Communicate risks associated with any activity, technology, or processes as you identify them.
  • Stay up to date with developments in the machine learning industry.

PythonSoftware DevelopmentSQLMachine LearningPyTorchAlgorithmsData StructuresTensorflowCollaborationCI/CDTime Management

Posted 2024-11-07
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