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Tier-1 brand, metro location, senior but popular ML title, broad LLM/MLOps skill demands.
Core ML skills are transferable, but financial-services and governance preferences increase domain specificity.
Explicit 8–12 years, deep ML/LLM/MLOps expertise and domain preferences make filters highly selective.
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Own end-to-end AI/ML initiatives including design, development, deployment, and continuous improvement of scalable advanced AI models such as LLMs, Generative AI, and multimodal systems.
Lead and drive large data science projects by collaborating with operations, technology, and business stakeholders to deliver measurable business value and product innovations in financial analytics.
Stay updated and apply emerging AI technologies and best practices including LLMOps, MLOps, AI Agents, synthetic data generation, ensuring high-quality standards and compliance.
8–12 years of experience in data science/AI with ownership of complex AI/ML projects.
Master’s degree in Statistics, Mathematics, Computer Science, or Engineering with specialization in Data Science/AI.
Strong expertise in Python, ML/DL frameworks, NLP, LLMs, Retrieval-Augmented Generation, and cloud platforms (AWS/Azure).
Experience with production-grade AI model deployment, MLOps/LLMOps, CI/CD workflows, and data engineering for complex structured and unstructured datasets.
Experienced in driving technical strategy and operating in multi-team or matrixed global environments, preferably within financial services or investment banking.
Demonstrated ability to translate complex business requirements into scalable AI solutions and influence cross-functional stakeholders as a technical authority.
Strong skills in advanced AI research, enterprise AI governance, and adoption of emerging AI technologies like Generative AI, AI Agents, and synthetic data techniques.