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AI/ML Engineer

Posted 11 days agoInactiveViewed

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πŸ’Ž Seniority level: Junior, 2+ years

πŸ“ Location: India

πŸ” Industry: Real Estate

🏒 Company: RealπŸ‘₯ 51-100MortgageInformation TechnologyReal Estate

πŸ—£οΈ Languages: English

⏳ Experience: 2+ years

πŸͺ„ Skills: PythonSQLData AnalysisMachine LearningNumpyPyTorchAlgorithmsPandasTensorflowRESTful APIsData visualizationData modeling

Requirements:
  • Strong programming skills in Python and familiarity with relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
  • Solid understanding of machine learning algorithms, statistical analysis, and data modeling techniques.
  • Proficiency in data manipulation and analysis using tools such as Pandas, NumPy, and SQL.
  • Ability to identify complex problems, analyze options, and implement effective solutions in a dynamic environment.
  • Excellent interpersonal skills, with the ability to communicate technical concepts to non-technical stakeholders effectively.
  • Willingness to learn and adapt to new technologies and methodologies as the field of AI/ML evolves.
  • Experience in managing multiple projects simultaneously, with a focus on meeting deadlines and delivering high-quality work.
  • Minimum of 2 years of experience working with Python in production environments.
  • At least 2 years of experience in the AI/ML domain.
  • Bachelor's degree in Technology or related field
Responsibilities:
  • Design, develop, and deploy AI/ML models to enhance real estate operations, customer experience, and product innovation.
  • Work closely with various teams (e.g., operations, marketing, product development) to identify and prioritize opportunities for AI integration.
  • Build and maintain scalable AI systems using Python and relevant frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
  • Stay informed about the latest trends and advancements in AI/ML to drive innovation and maintain competitive advantage.
  • Troubleshoot, optimize, and refine AI models to ensure high performance, reliability, and accuracy in production environments.
  • Maintain thorough documentation of models, processes, and results; communicate findings and progress to stakeholders.
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