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Senior Data Scientist – Gen AI Engineer

https://www.citi.com/ Logo

Citi

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Location:
India , Pune

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

Working at Citi is far more than just a job. A career with us means joining a team of more than 230,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.

Job Responsibility:

  • Working with financial data and applying NLP techniques, refining prompt engineering strategies for LLMs, collaborating with stakeholders to understand their needs, developing and testing Python code for GenAI solutions, integrating with vector databases, monitoring MLOps pipelines, researching emerging GenAI technologies, and participating in team meetings.
  • Troubleshooting and debugging GenAI models in production.
  • Staying up-to-date with the rapidly evolving GenAI landscape.
  • Communicating technical concepts clearly to non-technical stakeholders.

Requirements:

  • Master's degree in Computer Science, Data Science, or a related field. Ph.D. preferred.
  • 8-12 years of experience in a Data Scientist or equivalent role, with at least 4 years of specialized experience in Generative AI, including experience leading technical development and mentoring teams.
  • Working with financial data and applying NLP techniques, refining prompt engineering strategies for LLMs, collaborating with stakeholders to understand their needs, developing and testing Python code for GenAI solutions, integrating with vector databases, monitoring MLOps pipelines, researching emerging GenAI technologies, and participating in team meetings.
  • Troubleshooting and debugging GenAI models in production.
  • Staying up-to-date with the rapidly evolving GenAI landscape.
  • Communicating technical concepts clearly to non-technical stakeholders.
  • Candidates must possess demonstrable experience in the full lifecycle of real-world, production-level GenAI project implementation.
  • Data structures (lists, dictionaries, sets), algorithms, object-oriented programming, file handling, exception handling.
  • Scientific Computing: NumPy, Pandas, SciPy.
  • Machine Learning: Scikit-learn, XGBoost, LightGBM.
  • Deep Learning: TensorFlow, PyTorch.
  • GenAI: Transformers, LangChain.
  • GenAI Specific Skills, LLM architectures, RAG, Advanced Chatbot Architecturesm Prompt Engineeringm, Fine-tuning LLMs, API Development
  • Expert-level Python skills are mandatory.
  • Proven ability to design, develop, and deploy complex GenAI projects, from initial concept to production and maintenance.
  • Deep understanding of LLMs, RAG, and advanced chatbot architectures, with practical experience building and deploying robust solutions.
  • Expert-level programming skills in Python, including extensive experience using relevant libraries like TensorFlow, PyTorch, Transformers, and LangChain.
  • Experience designing and implementing scalable API architectures for GenAI applications.
  • Extensive knowledge of Machine Learning concepts, including advanced modeling techniques, model evaluation, and deployment strategies.
  • Expertise in prompt engineering, fine-tuning LLMs, and optimizing performance for specific use cases.
  • Strong technical leadership and mentorship skills, with a demonstrated ability to guide and support other data scientists.
  • Excellent communication and collaboration skills, both written and verbal, with the ability to effectively convey technical concepts to diverse audiences.

Nice to have:

  • Experience with MLOps and building robust AI pipelines.
  • Deep understanding of cloud computing platforms and their application in GenAI.
  • Experience with developing and deploying conversational AI solutions in production environments.
  • Contributions to research and publications in the field of Generative AI.
  • Experience building and managing large datasets for training GenAI models.

Additional Information:

Job Posted:
May 04, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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