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Data Science Engineer - Generative AI
Data Science Engineer - Generative AITalent Worx • Hyderabad
Data Science Engineer - Generative AI

Data Science Engineer - Generative AI

Talent Worx • Hyderabad
2 days ago
Job description

Description :

Key Responsibilities :

  • Define the AI strategy and roadmap, including tooling selection, experimentation frameworks, and best practices for model development, prompt engineering, and fine-tuning.
  • Architect, develop, and deploy large-scale Machine Learning and Generative AI (GenAI) pipelines from ideation through production.
  • Lead end-to-end project ownership, from problem identification and data acquisition to model design, evaluation, and deployment.
  • Collaborate cross-functionally with engineering, product, and business teams to translate business goals into actionable data science initiatives.
  • Mentor and guide junior data scientists, fostering a culture of continuous learning, experimentation, and innovation.
  • Evaluate emerging AI technologies and frameworks to enhance organizational capabilities and technical maturity.

Essential Qualifications :

  • 35 years of relevant experience in Data Science, Machine Learning, or Artificial Intelligence.
  • Hands-on experience with Large Language Models (LLMs) such as OpenAI GPT, Anthropic Claude, or LLaMA including prompt engineering, fine-tuning, and embedding-based retrieval systems.
  • Expert proficiency in Python and key libraries : NumPy, Pandas, scikit-learn, PyTorch / TensorFlow, and Hugging Face Transformers.
  • Proven track record of delivering at least one end-to-end GenAI or advanced NLP project (e.g., custom NER, Q&A systems, text summarization) into production.
  • Strong understanding of model deployment and orchestration tools such as Docker, Kubernetes, Airflow, and API frameworks (Flask, FastAPI).
  • Solid foundation in statistical modeling, data analysis, and MLOps best practices.
  • Preferred Qualifications :

  • Masters or Ph.D. in Computer Science, Statistics, Data Science, or a related quantitative discipline.
  • Experience working in cloud environments (AWS, Azure, or GCP) for scalable model training and deployment.
  • Familiarity with vector databases, retrieval-augmented generation (RAG), or multi-modal AI systems.
  • Strong communication skills with the ability to translate complex technical findings into actionable business insights
  • (ref : hirist.tech)

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