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Metro Bangalore, popular Data Scientist title, mid-level experience, and broad Generative AI/CV requirements increase competition.
Core ML, CV and Generative AI skills are transferable, but industrial/manufacturing context adds moderate specificity.
Explicit 3-5 years plus mandatory ML/CV, Generative AI platforms, and data-engineering skills make filters strict.
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Collaborate with AI team to design and deploy machine learning, deep learning, and Generative AI models for multimodal data including images, text, and documents.
Develop and operationalize LLM-based AI applications such as intelligent document processing, semantic search, summarization, and retrieval-augmented generation (RAG).
Build and maintain scalable data pipelines and workflows for data ingestion, transformation, feature engineering, and model deployment using ML Ops and LLM Ops best practices.
3-5 years of experience as a Data Scientist or in a related role.
Proficiency in Python and libraries including NumPy, pandas, scikit-learn; experience with open-source OCR models.
Hands-on experience in computer vision, image segmentation, anomaly detection, and Generative AI techniques such as embeddings and fine-tuning.
Familiarity with Generative AI platforms like Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI, Anthropic, or Hugging Face, and knowledge of data engineering concepts including ETL/ELT workflows and feature pipeline development.
Experienced in end-to-end deployment of AI/ML models in production environments with expertise in ML Ops/LLM Ops.
Strong background in multimodal AI applications integrating computer vision and natural language processing technologies.
Capable of working with cloud-based Generative AI services and data engineering workflows supporting scalable AI system development.