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Strong global brand, popular GenAI role, metro location, and broad skill requirements increase competition.
Specialized GenAI and enterprise fintech requirements make cross-industry transfers difficult.
Requires deep GenAI production experience, specific tools, and leadership, yielding high shortlisting strictness.
Lead architecture and development of enterprise-scale generative AI systems including multi-agent AI and multimodal generative pipelines.
Deliver production-grade Retrieval-Augmented Generation (RAG) systems and LLM fine-tuning strategies optimizing model alignment for specialized use cases.
Establish robust LLMOps and MLOps pipelines to ensure secure, scalable, and continuous AI delivery with responsible AI governance implementation.
Experience: Significant hands-on experience leading and delivering complex generative AI or ML engineering programs in production.
Technical Skills: Expertise with LLM ecosystems (OpenAI, Anthropic, Gemini, Hugging Face, LangChain/LangGraph), Python programming (including async and API development), and cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI).
Education: Bachelor's or Master's degree in Computer Science, AI/ML, or Engineering.
Work Experience Required: Not explicitly mentioned in the JD.
Proven technical leadership in designing, building, and deploying large language model applications and agentic AI systems from prototype to production.
Strong applied knowledge of combining generative AI with traditional ML/statistical models for hybrid interpretable decision systems.
Experienced in operating within cutting-edge AI development environments involving multi-modal data, vector databases, and advanced model operationalization on cloud platforms.