Sr. Data Scientist Gen AI Engineer - Assistant Vice President

The ideal candidate for the position should have a Master's degree in Computer Science, Data Science, or a related field, with a preference for a Ph.D. They should have 8-12 years of experience in a Data Scientist or equivalent role, including at least 4 years of specialized experience in Generative AI. This experience should involve leading technical development, mentoring teams, and troubleshooting and debugging GenAI models in production. Additionally, candidates should have experience working with financial data, applying NLP techniques, developing and testing Python code for GenAI solutions, integrating with vector databases, and monitoring MLOps pipelines. It is essential for the candidate to stay up-to-date with the rapidly evolving GenAI landscape, continuously learn new tools and techniques, and communicate technical concepts clearly to non-technical stakeholders. Candidates must possess demonstrable experience in the full lifecycle of real-world, production-level GenAI project implementation, including deploying, monitoring, and maintaining models in a live environment. Proof-of-concept work alone is not sufficient. Candidates should have expertise in various technical areas such as data structures, algorithms, object-oriented programming, scientific computing (NumPy, Pandas, SciPy), machine learning (Scikit-learn, XGBoost, LightGBM), deep learning (TensorFlow, PyTorch), and GenAI (Transformers, LangChain). Specific skills in LLM architectures, RAG, Advanced Chatbot Architectures, Prompt Engineering, Fine-tuning LLMs, and API Development are required. Expert-level Python skills are mandatory, including proficiency in core Python and key libraries for AI/ML and GenAI applications. The candidate should have proven abilities in designing, developing, and deploying complex GenAI projects, a deep understanding of LLMs, RAG, and advanced chatbot architectures, and experience in designing scalable API architectures for GenAI applications. Preferred qualifications include experience with MLOps, cloud computing platforms, conversational AI solutions, research contributions in Generative AI, and building and managing large datasets for training GenAI models. Strong technical leadership, mentorship skills, and excellent communication and collaboration skills are also essential for this role. Overall, the candidate should be able to articulate the challenges and successes of deploying GenAI solutions at scale, effectively convey technical concepts to diverse audiences, and work collaboratively with stakeholders to meet their needs.,