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AI AND DATA Scientist Engineer

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Onsite

AI AND DATA Scientist Engineer

Coreehs

AI AND DATA Scientist Engineer

Skills

SQL QueriesBig DataDeep LearningTableauPythonData ScienceData VisualizationJavaKerasPyTorchTensorFlowRData AnalysisMachine LearningAINLP / Natural Language ProcessingHadoopPower BI

3 months ago

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Important information


Contract type:

Permanent

Location:

Pune, Maharashtra, India

Work mode:

Onsite

Published on:

7 August 2026

What they need


  1. AI Development:
  • Design, develop, and deploy AI algorithms and machine learning models for safety systems, predictive analytics, and process optimization.
  • Implement computer vision and NLP solutions for AR/VR applications and industrial safety scenarios.
  1. Data Analysis and Modelling:
  • Collect, preprocess, and analyse large datasets from industrial systems and safety processes.
  • Build predictive models, recommendation systems, and clustering algorithms to solve business challenges.
  1. Integration and Deployment:
  • Integrate AI/ML models into existing software systems, AR/VR platforms, and training simulators.
  • Develop APIs and tools to make AI capabilities accessible across platforms.
  1. Visualization and Reporting:
  • Create intuitive dashboards and visualizations to present data insights to stakeholders.
  • Generate detailed reports explaining analytical findings and model performance.
  1. Research and Innovation:
  • Explore and experiment with new AI technologies and frameworks to improve safety and operational efficiency.
  • Conduct feasibility studies for integrating emerging AI techniques into industrial solutions.
  1. Collaboration:
  • Work closely with software developers, AR/VR teams, and business stakeholders to align AI solutions with company goals. Role & responsibilities

Preferred candidate profile

  • Experience: 5-7 years in AI, data science, or related fields with proven experience deploying machine learning models in production.
  • Programming Skills: Proficiency in Python, R, or Java with experience using libraries like TensorFlow, PyTorch, Scikit-learn, or Keras.
  • Data Tools: Strong knowledge of SQL/NoSQL databases, Hadoop, Spark, or other big data platforms.
  • AI/ML Expertise: Hands-on experience with supervised, unsupervised, and deep learning techniques, as well as reinforcement learning.
  • Analytics and Visualization: Expertise in tools like Tableau, Power BI, or Matplotlib for data visualization.

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