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Minimum Qualifications:
Troubleshooting and debugging GenAI models in production. This often requires quickly identifying and resolving issues that arise in real-world deployments.
Staying up-to-date with the rapidly evolving GenAI landscape. Continuous learning and exploration of new tools and techniques are essential.
Communicating technical concepts clearly to non-technical stakeholders. This involves translating complex AI/ML jargon into understandable business terms.
Candidates must possess demonstrable experience in the full lifecycle of real-world, production-level GenAI project implementation. This includes not just developing models but also deploying, monitoring, and maintaining them in a live environment. Proof-of-concept work alone is insufficient. Look for candidates who can articulate the challenges and successes of deploying GenAI solutions at scale.
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. This includes deep familiarity with core Python, as well as proficiency in key libraries for AI/ML and GenAI applications:
Preferred Qualifications:
Education:
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
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View Citi’s EEO Policy Statement and the Know Your Rights poster.
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