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Tier-1 brand, mid-level ML role, metro location, and broad skillset increase candidate competition.
Core ML skills transfer across industries, but fintech/payments domain and compliance needs raise fit sensitivity to medium.
Explicit 5+ years plus mandatory LLM, MLOps, cloud and specific tooling requirements enforce rigorous screening.
Build and manage end-to-end predictive models and AI agents focused on payment optimization, agent-native operations, and conversational AI within financial services.
Leverage and integrate advanced Large Language Models (LLMs) and generative AI for next-gen payment experiences including prompt engineering and fine-tuning.
Collaborate with engineering, compliance, and operations teams to deploy and optimize scalable AI/ML solutions across regional markets.
5+ years of relevant experience in data science or ML engineering roles.
Expertise in Python, Spark, SQL (Presto/Hive), and core machine learning concepts including Bagging, Boosting, Online Learning, and Recommendation Engines.
Hands-on experience with agentic frameworks such as LangGraph, LangSmith, or LangChain and LLM orchestration.
Experience in MLOps using tools like MLflow, Kubeflow, SageMaker, including production deployment and cloud platform scalability.
Strong background in financial services AI use cases, particularly payments, lending, or insurance domains across Southeast Asia markets.
Demonstrated ability to independently own AI/ML solutions lifecycle from design through production in a collaborative tech and business environment.
Operates well in relatively flat teams focusing on complex ML problems involving batch, real-time, and generative AI workloads.