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Tier-1 brand increases applicants, but senior niche ML/LLM focus limits density.
Requires deep ML/LLM expertise plus finance domain experience, limiting cross-industry transferability.
Stringent 10+ years requirement and extensive mandatory ML, LLM, MLOps, and financial domain skills.
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Lead the full lifecycle of AI/ML model development, including design, building, deployment, and adoption, focusing on enterprise-scale data reconciliation in Capital Markets.
Develop and implement Agentic AI and Generative AI solutions addressing complex financial data engineering challenges across global operations.
Collaborate with cross-functional teams to ensure model integration, scalability, performance, and provide strategic data-driven insights for senior leadership decisions.
10+ years hands-on AI/ML development and big data engineering experience in Financial Services, Insurance, or Telecom domains.
Expert proficiency in Python (scikit-learn, TensorFlow, PyTorch), R, and SQL, with strong knowledge of supervised and unsupervised ML algorithms.
Experience building and deploying Agentic AI and large language model-based solutions using LangGraph, LangChain, ADK, and MLOps tools like Apache Airflow, Kubernetes, Docker.
Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or related fields.
Senior data scientist with deep expertise in AI/ML and big data technologies, experienced in developing financial data reconciliation frameworks at enterprise scale.
Proficient with advanced ML model orchestration and deployment, capable of managing complex Agentic AI and LLM projects involving cloud and containerized environments.
Strategic operator skilled at translating technical insights into actionable recommendations for C-suite and cross-functional stakeholders.