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