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Tier-1 brand, metro locations, and mid-level generalist range increase candidate competition.
Core GenAI/ML skills transfer across industries, but financial-services domain and compliance preferences increase specificity.
Explicit 5–8 years plus deep GenAI, ML, and MLOps requirements tighten candidate filters.
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Lead end-to-end design, implementation, testing, deployment, and support of AI-powered solutions focused on client experience and operational business problems in Treasury & Trade Services.
Apply Generative AI and deep learning techniques including prompt engineering, Retrieval-Augmented Generation, and LLM fine-tuning to build production-grade AI applications with quality and safety guardrails.
Collaborate cross-functionally with product, data, security, and platform teams to translate ambiguous business problems into data-driven insights and AI capabilities affecting strategic priorities.
5-8 years experience in Data Science with focus on ML, DL, and Generative AI solutions including LLMs and transformer architecture.
Proven track record shipping AI-enabled products to production in agile environments, preferably in financial services.
Hands-on experience with PyTorch/TensorFlow, Hugging Face, prompt engineering, RAG, model customization and MLOps for GenAI.
Masters degree in Computer Science Engineering preferred. Work Experience Required: 5-8 years explicitly mentioned.
Strong technical expertise in building scalable, secure AI/ML applications with focus on GenAI and large language models in a financial industry context.
Experience working on interdisciplinary teams and translating vague business challenges into analytical frameworks and AI-enabled solutions.
Proven ability to implement robust quality, safety, compliance, and monitoring practices in production AI applications impacting sensitive business operations.