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Tier-1 brand and metro location raise competition, but senior specialized ML/AI focus reduces density.
Strong financial-services and reconciliation domain expertise required, limiting transferable applicants across industries.
10+ years requirement plus mandatory LLM, MLOps, and financial reconciliation expertise create high shortlisting strictness.
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Lead design, build, and deployment of AI/ML models including Agentic and Generative AI for enterprise-scale data reconciliation and optimization within Capital Markets operations.
Own end-to-end ML lifecycle: requirements, data preprocessing, modeling, validation, production integration, and adoption, ensuring model scalability and performance in production.
Analyze large structured and unstructured financial datasets; define ML roadmaps aligned with business and technical teams; communicate risks and recommendations to senior stakeholders.
10+ years hands-on AI/ML and big data engineering experience in Financial Services, Insurance, or Telecom sectors.
Expert-level programming in Python (scikit-learn, TensorFlow, PyTorch, Pandas, NumPy), R, and SQL; strong ML algorithm knowledge including neural networks and ensemble models.
Proven experience building and deploying Agentic AI and LLM-based solutions using LangGraph, LangChain, and Agent Development Kit.
Strong production-level MLOps experience with Apache Airflow, Kubernetes, Docker plus distributed computing (Apache Spark, Hadoop) and cloud-native platforms (AWS S3, Amazon Redshift).
Senior data scientist with deep experience in financial data reconciliation and enterprise-scale AI/ML solution architecture.
Technical leader comfortable driving cross-functional collaboration with engineering, operations, and business stakeholders to implement scalable, production-grade ML models.
Pragmatic problem solver with a balance of advanced ML expertise and practical production deployment experience within regulated financial environments.