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Tier-1 bank and metro hiring increase competition, but seniority and niche ML/finance expertise moderate applicant density.
Requires deep financial-services domain knowledge and specialized ML/LLM expertise, limiting industry transferability.
Explicit 10+ years, mandatory financial-services ML expertise and extensive tech stack enforce strict candidate filters.
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Lead end-to-end AI/ML model development lifecycle including design, building, deployment, and integration at enterprise scale within Capital Markets operations, risk, and finance.
Develop advanced AI/ML solutions including Agentic AI and Generative AI to solve complex data reconciliation and engineering challenges.
Collaborate with cross-functional teams to ensure model scalability, performance, and alignment with business and technical roadmaps.
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), R, SQL, and advanced ML algorithms including neural networks and ensemble methods.
Experience with Agentic AI and LLM-based solutions implementing LangGraph, LangChain, and ADK frameworks.
Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or related field.
Senior technical leader with demonstrated ability to deliver complex AI/ML solutions at enterprise scale in financial domain.
Experienced in deploying production-grade ML models using MLOps tools and cloud-native data platforms (AWS, Azure, Google).
Adept at managing cross-disciplinary stakeholder requirements and translating complex data insights into strategic recommendations for senior leadership.