Sr. Lead Software Engineer - Python, AWS, AI
Senior lead engineer on JPMorgan's GB Intelligence team building production-grade AI for Global Banking: Python backend services and APIs, agentic workflows via Smart SDK, end-to-end RAG pipelines on OpenSearch, LLM/agent evaluation and monitoring, and data layers on AWS Aurora, with cloud-native AWS infrastructure using Terraform.
- Compensation
- Not disclosed
- Location
- Glasgow, Scotland, United Kingdom
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Formal training or certification on software engineering concepts and advanced applied experience
- Strong hands-on expertise in Python, building production backend systems (API/service development, testing, performance debugging, dependency management)
- Experience building agentic and LLM-powered capabilities, including tool integration, orchestration patterns, and production-grade safety/reliability guardrails
- Experience implementing evaluation and measurement loops for LLM and agent quality (relevance/grounding checks, regression testing, reliability and latency monitoring)
- Experience across the full SDLC, from design and implementation through testing, deployment, and production support
- Strong written and verbal communication skills, able to explain technical tradeoffs to cross-functional partners
- Proficiency with AWS, Terraform, GitHub, Jenkins, and modern developer tooling
- Experience with containerization, including Docker and orchestration platforms such as ECS, EKS, or Kubernetes
- Expertise with monitoring and observability tools such as Splunk, Datadog, Dynatrace, or CloudWatch
- Hands-on experience using enterprise-authorized AI-assisted software development tools, with ability to validate and refine AI-generated outputs
- Understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs/outputs
Nice to have
- Experience with Redis in production services, including caching, rate limiting, and coordination patterns
- Experience scaling and hardening RAG systems, including relevance tuning and prompt/response pattern optimization
- Prior experience integrating AI services with enterprise systems used in banking or financial services workflows
- Exposure to agile engineering practices including CI/CD, application resiliency, and secure engineering
Full description
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