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Strong employer brand and metro location raise competition, but senior niche role narrows applicant pool.
Requires deep financial services experience and specialized ML/LLM toolset, limiting cross-industry transferability.
Explicit 10+ years, financial-services domain requirement, and extensive mandatory ML/MLOps tech stack increase strictness.
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Lead end-to-end AI/ML model development lifecycle including design, deployment, and adoption focused on enterprise-scale data reconciliation and engineering challenges.
Drive measurable impact in Capital Markets operations, risk, and finance by applying advanced AI including Agentic AI and Generative AI solutions.
Collaborate with cross-functional teams to ensure seamless model integration, scalability, risk mitigation, and deliver strategic insights from complex financial data.
10+ years hands-on AI/ML development and big data engineering experience in Financial Services, Insurance, or Telecom.
Expert proficiency in Python (scikit-learn, TensorFlow, PyTorch, Pandas, NumPy), R (caret, tidyverse, mlr3), and SQL (PostgreSQL, Oracle, MySQL).
Production experience with Agentic AI and LLM-based solutions using LangGraph, LangChain, and Agent Development Kit (ADK).
Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, Information Systems, Mathematics, Statistics, or related field.
Experienced leader driving AI/ML projects impacting large-scale financial data reconciliation and operational processes in global enterprises.
Proven ability to manage complex ML workflows and infrastructure including MLOps tools (Airflow, Kubernetes, Docker) and distributed computing (Spark, Hadoop).
Strong collaborator skilled at translating data insights into actionable recommendations for senior business and technology stakeholders.