GB Intelligence is redefining how Global Banking operates—bringing AI-driven insights and workflow automation into the core of deal origination, market and sector intelligence, and client coverage. This is a rare opportunity to build production-grade AI capabilities that directly shape how bankers work, at the scale and complexity that only JPMorganChase can offer. You'll grow alongside a team of engineers who are passionate about turning cutting-edge technology into trusted, real-world impact.
As a Senior Lead Software Engineer at JPMorganChase within GB Intelligence, you will be a hands-on engineer building AI capabilities that integrate directly into bankers' daily workflows. You will engineer secure services and data flows, develop agentic workflows, and help transform complex client, deal, and market data into trusted insights—operationalized across downstream systems to accelerate execution, strengthen risk discipline, and elevate the day-to-day experience for Global Banking teams. Your work will sit at the intersection of modern AI engineering and enterprise-grade reliability, where the quality of what you build has direct business consequence.
Job responsibilities
- Design and implement complex backend services and APIs in Python, applying sound engineering judgment and pragmatic architecture decisions that scale across production environments
- Build and refine agentic capabilities using the Smart SDK, including tool integration, orchestration patterns, and safety and reliability guardrails suitable for production use
- Develop and optimize retrieval-augmented generation (RAG) pipelines end-to-end—covering ingestion, chunking, embeddings, retrieval, reranking, and prompt/response patterns—improving relevance, latency, and robustness with OpenSearch and continuous measurement
- Implement and maintain large language model (LLM) and agent evaluation and monitoring frameworks, including offline test sets, automated regression checks, relevance and grounding scoring, and production dashboards to continuously improve quality, safety, latency, and cost
- Engineer secure, reliable integrations with systems bankers rely on daily, ensuring traceability and operational readiness across the platform
- Write secure, high-quality production code and raise the bar through code reviews, debugging, and hands-on mentorship—improving maintainability, performance, and consistency across the codebase
- Drive operational excellence by identifying recurring issues and implementing automation, preventative controls, and reliability improvements to reduce toil and improve system stability
- Engineer data and search solutions using AWS Aurora and OpenSearch—including schema design, migrations, query tuning, indexing strategies, and query relevance tuning—to support AI and analytics workflows
- Contribute to cloud-native engineering on AWS, partnering on infrastructure-as-code with Terraform and improving deployment safety, environment consistency, and observability
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Strong hands-on expertise in Python, building production backend systems including API and service development, testing practices, performance debugging, and dependency management
- Demonstrated experience building agentic and LLM-powered capabilities, including tool integration, orchestration patterns, and production-grade safety and reliability guardrails
- Experience implementing evaluation and measurement loops for LLM and agent quality, including relevance and grounding checks, regression testing, and reliability and latency monitoring
- Experience across the full Software Development Life Cycle (SDLC), from design and implementation through testing, deployment, and production support
- Strong written and verbal communication skills, with the ability to explain technical tradeoffs clearly to cross-functional partners
- Proficiency with AWS, Terraform, GitHub, Jenkins, and modern developer tooling
- Experience with containerization, including Docker and container orchestration platforms such as Amazon Elastic Container Service (ECS), Amazon Elastic Kubernetes Service (EKS), or Kubernetes
- Demonstrated expertise with monitoring and observability tools such as Splunk, Datadog, Dynatrace, or CloudWatch, with proven ability to influence high-performing teams by driving innovation
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred qualifications, capabilities, and skills
- Experience with Redis in production services, including caching, rate limiting, and coordination patterns
- Experience scaling and hardening RAG systems, including relevance tuning, prompt and response pattern optimization, and robustness improvements
- Prior experience integrating AI services with enterprise systems used in banking or financial services workflows
- Exposure to agile engineering practices including continuous integration and delivery (CI/CD), application resiliency, and secure engineering
Skills Required
- Formal training or certification in software engineering concepts and advanced applied experience
- Strong hands-on expertise in Python and production backend systems, including API and service development, testing, performance debugging, and dependency management
- Experience building agentic and LLM-powered capabilities, including tool integration, orchestration, safety, and reliability guardrails
- Experience implementing LLM and agent evaluation and measurement loops, including relevance, grounding, regression testing, reliability, and latency monitoring
- Experience across the full Software Development Life Cycle, including design, implementation, testing, deployment, and production support
- Strong written and verbal communication skills and ability to explain technical tradeoffs to cross-functional partners
- Proficiency with AWS, Terraform, GitHub, Jenkins, and modern developer tooling
- Experience with Docker and container orchestration platforms such as Amazon ECS, Amazon EKS, or Kubernetes
- Expertise with monitoring and observability tools such as Splunk, Datadog, Dynatrace, or CloudWatch
- Ability to influence high-performing teams by driving innovation
- Hands-on experience using enterprise-authorized AI-assisted software development tools for coding, testing, troubleshooting, or documentation
- Ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use, data sensitivity, secure input and output handling, resiliency, and security expectations
- Ability to guide peers on safe and effective AI-assisted engineering practices
- Production experience with Redis for caching, rate limiting, or coordination patterns
- Experience scaling and hardening RAG systems, including relevance tuning, prompt and response optimization, and robustness improvements
- Experience integrating AI services with enterprise banking or financial services systems
- Exposure to agile engineering, CI/CD, application resiliency, and secure engineering
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Health coverage is considered comprehensive, including medical, dental, and vision, alongside wellness and mental health resources. Some locations add onsite health centers and related wellbeing support.
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Retirement Support — Retirement offerings include a 401(k)-type savings plan and related financial benefits, with options such as employee stock purchase participation. Financial planning resources are also highlighted to support long-term savings.
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Parental & Family Support — Paid parental leave of 16 weeks for birth or adoption is available for all parents. Child care and back-up child care resources further reinforce family support.
JPMorganChase Insights
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
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