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Tier-1 employer, mid-level 5-8yr role, metro location, and popular GenAI skills increase applicant density.
Core GenAI/MLOps skills are transferable across industries, though fintech domain experience is preferred.
Explicit 5-8 years, 2 years GenAI, plus specific ML/MLOps and production requirements create strict filters.
Lead end-to-end delivery of AI-powered products involving design, implementation, testing, deployment, and support.
Develop and apply Generative AI (GenAI) and deep learning techniques including prompt engineering, RAG, and fine-tuning to solve client experience and operational business problems.
Collaborate across product, business, operations, and data teams to translate complex, ambiguous problems into AI solutions and provide data-driven insights using multiple structured and unstructured data sources.
5 to 8 years of relevant Data Science experience including at least 2 years with Generative AI solutions.
Master's degree preferred in Computer Science Engineering.
Substantial experience with LLMs, transformer architectures, prompt engineering, Retrieval-Augmented Generation (RAG), model customization (fine-tuning / LoRA / PEFT), and frameworks like PyTorch/TensorFlow and Hugging Face.
Experience identifying and resolving business problems related to client experience and operations, preferably within financial services.
Experienced in shipping AI-enabled products to production within an agile environment, indicating strong operational delivery capability.
Strong background in handling diverse data sources (structured and unstructured) and using advanced GenAI tools and MLOps practices for scalable model deployment.
Comfortable working in cross-functional teams converting ambiguous and open-ended business problems into analytical and AI-driven solutions.