C
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
- 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.
- 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.
- 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.
- Visualization and Reporting:
- Create intuitive dashboards and visualizations to present data insights to stakeholders.
- Generate detailed reports explaining analytical findings and model performance.
- 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.
- 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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