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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro location, mid-level generalist GenAI role with broad skillset increases competition.
Requires specialized GenAI/ML expertise and banking domain knowledge, so industry-specific background is strongly preferred.
Explicit 5–8 years requirement plus mandatory LLM, MLOps, and fintech experience makes filters highly strict.
Job Description
Structured overview of role & requirementsAbout This Role
Lead end-to-end delivery of AI-powered products, including design, development, testing, deployment, and support within the TTS Analytics team.
Conduct multiple analyses leveraging diverse data sources (unstructured and structured) and apply GenAI and deep learning techniques to solve business problems related to client experience and operations in financial services.
Collaborate with cross-functional teams to translate ambiguous problems into analytical solutions, participate in documentation and code reviews, and ensure compliance with regulatory and ethical standards.
Minimum Requirements
5 to 8 years relevant experience in Data Science including ML and DL, with at least 2 years in Generative AI solutions.
Hands-on expertise with LLMs, transformer architectures, prompt engineering, Retrieval-Augmented Generation (RAG), and model customization techniques.
Proficiency with frameworks like PyTorch/TensorFlow, Hugging Face, plus skills in model serving, optimization, MLOps for GenAI, and handling data from multiple structured/unstructured sources.
Master's degree preferred in Computer Science or Engineering; experience shipping AI-enabled products in production and working in financial services domain.
Ideal Candidate Profile
Experienced in applying GenAI and deep learning approaches to strategic business problems, particularly in enhancing client experience and operations within financial services.
Operates effectively in ambiguous and complex scenarios, capable of ideating and delivering scalable AI-driven analytics solutions that integrate multiple data types and advanced modeling techniques.
Comfortable collaborating across product, business, operations, and data teams, with strong capability in technical documentation, solution architecture, risk assessment, and regulatory compliance.