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Engineering Manager - AI Applications

Posted 2024-11-08

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💎 Seniority level: Manager, 7+ years of software engineering experience, with at least 3+ years focused on developing production-grade applications, and 2+ years in a leadership or management role

📍 Location: France

🔍 Industry: Video Games

⏳ Experience: 7+ years of software engineering experience, with at least 3+ years focused on developing production-grade applications, and 2+ years in a leadership or management role

🪄 Skills: AWSLeadershipProject ManagementPythonGCPJavaKerasKubeflowMachine LearningMLFlowProduct ManagementPyTorchC++StrategyAzureTensorflowCommunication SkillsCollaboration

Requirements:
  • Bachelor's or Master’s degree in Computer Science, Engineering, AI/ML, or a related field. Advanced degrees in AI or machine learning are a plus.
  • 7+ years of software engineering experience, with at least 3+ years focused on developing production-grade applications.
  • 2+ years in a leadership or management role with proven track record in managing engineering teams.
  • Strong proficiency in programming languages such as Python, Java, or C++.
  • Familiarity with cloud platforms (AWS, GCP, Azure) and deploying AI applications in cloud environments.
  • Experience with AI frameworks such as TensorFlow, PyTorch, or Keras, and MLOps tools like Kubeflow or MLflow.
  • Strong leadership and mentorship abilities with a passion for developing talent.
  • Excellent communication skills for translating complex concepts to non-technical stakeholders.
  • Strong problem-solving skills in AI application development.
Responsibilities:
  • Oversee the design, development, and deployment of AI applications, ensuring alignment with business goals and technical standards.
  • Manage the end-to-end delivery of AI application projects, ensuring completion on time, within scope, and quality.
  • Collaborate closely with product managers, ML Engineers, and other teams to define requirements and integrate AI features.
  • Lead, mentor, and develop a team of engineers focused on building AI applications.
  • Contribute to AI application strategy and identify new opportunities for integration and optimization.
  • Provide technical guidance on system architecture, data pipelines, and deployment processes.
  • Define KPIs and establish monitoring frameworks to track performance and resolve production issues.
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