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Mid-level GenAI role, metro location and popular ML skills increase applicant competition.
Generative AI and ML skills transfer across industries, banking preference creates moderate domain bias.
Explicit 4+ years plus mandatory generative AI, LLM, RAG, and framework skills tighten filters.
Design, build, and deploy production-ready Generative AI solutions using LLMs and Retrieval-Augmented Generation frameworks.
Develop AI applications such as chatbots, document summarization, and knowledge assistants with prompt engineering and embeddings.
Collaborate with cross-functional teams to deliver scalable enterprise-grade AI/ML models and ensure deployment and monitoring in production environments.
4+ years of experience in machine learning, deep learning, or AI research focused on generative models.
Strong hands-on expertise with Large Language Models (GPT-3/4, BERT, DALL.E), prompt engineering, embeddings, RAG, and AI orchestration frameworks like LangChain or LlamaIndex.
Proficiency in Python and ML libraries (Scikit-learn, TensorFlow, PyTorch), experience with APIs, model integration, vector databases, and cloud platforms (Azure, AWS, GCP).
Work Experience Required: 4+ years in relevant AI/ML roles; explicit experience in Corporate or Institutional Banking domain is preferred but not mandatory.
Experienced in delivering enterprise-scale Generative AI solutions with large models and RAG in production environments.
Comfortable working closely with business stakeholders and technical teams to translate complex business problems into scalable AI applications.
Familiar with AI governance, security, MLOps practices, and has a strategic orientation toward evolving Generative AI and ML trends.