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Tier-1 employer and metro location increase competition despite seniority and niche ML requirements.
Requires financial-services reconciliation experience and enterprise ML/MLOps expertise, limiting cross-industry transferability.
Explicit 10+ years, financial-services domain experience, and mandatory ML/MLOps stack make filters stringent.
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Lead the design, development, and deployment of AI/ML models including Agentic AI and Generative AI for enterprise-scale data reconciliation in Capital Markets operations.
Own the end-to-end machine learning lifecycle from data preprocessing, modeling, validation, to production integration with measurable impact on risk and finance processes.
Collaborate with technical and business teams to define ML roadmaps, ensure scalable production deployments, and communicate technical risks and insights to senior leadership.
10+ years of hands-on AI/ML and big data engineering experience in Financial Services, Insurance, or Telecom domains.
Expert proficiency in Python (scikit-learn, TensorFlow, PyTorch), R, SQL, and extensive experience with supervised and unsupervised ML algorithms.
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, Mathematics, Statistics, or related field.
Technical leader skilled at managing full ML model development lifecycle in complex, large-scale financial environments.
Experienced in modern MLOps technologies (Kubernetes, Apache Airflow, Docker) and distributed computing (Spark, Hadoop).
Strong collaborator able to align ML strategies with business goals and translate complex data insights for senior stakeholders.