Quant Analytics Analyst

JPMorgan Chase JPMorgan Chase · Banking · Metro Manila, National Capital Region, Philippines · Consumer & Community Banking

This role focuses on designing, building, and deploying production-grade AI applications, specifically agent-based systems and RAG implementations, integrated with cloud services and data stores. The role emphasizes transforming business needs into measurable outcomes using LLMs and advanced analytics within a fintech domain, requiring secure, scalable, and compliant solutions.

What you'd actually do

  1. Design and deliver production-grade applications powered by large language models and advanced analytics
  2. Build agent-based systems that plan, decompose, and execute complex workflows across tools and services
  3. Develop and optimize prompts, evaluation methods, and guardrails to improve reliability and user outcomes
  4. Implement retrieval-augmented generation patterns to ground model responses in trusted data sources
  5. Integrate applications with cloud services, data stores, and APIs to support end-to-end workflows

Skills

Required

  • Professional experience building and deploying modern applications in a production environment
  • Hands-on Python development experience, including writing maintainable, testable code
  • Demonstrated experience building intelligent applications using large language models, including prompt design and systematic evaluation
  • Demonstrated experience implementing agent frameworks (for example, Google Agent Development Kit or similar)
  • Demonstrated experience with retrieval-augmented generation solutions (indexing, retrieval strategies, grounding, and citations)
  • Hands-on experience with containerized development and deployment (for example, Docker and orchestration platforms)
  • Hands-on experience with Amazon Web Services core services for data and application workloads (for example, managed compute, storage, databases, and serverless)
  • Strong understanding of secure development practices, including identity and access controls and data handling principles
  • Ability to translate ambiguous business problems into technical designs, milestones, and measurable outcomes
  • Strong analytical and problem-solving skills, including root-cause analysis in production systems

Nice to have

  • Experience deploying and operating solutions on Kubernetes (for example, Amazon Elastic Kubernetes Service)
  • Experience with Amazon Bedrock or similar managed model platforms and orchestration patterns
  • Experience building data pipelines and analytics workflows (for example, AWS Glue and Amazon Athena)
  • Experience with relational database design and performance tuning (for example, Amazon Relational Database Service)
  • Experience implementing model fine-tuning or adaptation techniques with clear evaluation and governance
  • Experience partnering with risk, controls, privacy, and compliance teams on model-enabled products
  • Experience mentoring engineers and establishing team development standards and reusable patterns

What the JD emphasized

  • production-grade applications
  • agent-based systems
  • retrieval-augmented generation patterns
  • secure, scalable, and aligned to responsible and compliant use
  • production environment
  • large language models
  • agent frameworks
  • retrieval-augmented generation solutions

Other signals

  • design and deliver production-grade applications powered by large language models
  • build agent-based systems that plan, decompose, and execute complex workflows
  • implement retrieval-augmented generation patterns
  • integrate applications with cloud services, data stores, and APIs