Sr. Lead Software Engineer

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Technology Data Strategy & Architecture organization, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

• Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and debugs code written by others
• Leads end-to-end design and implementation of complex software features, from requirements through deployment and operational stability
• Drives technical decisions that influence application design, functionality, performance, and reliability
• Builds and maintains agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments
• Implements LLM-based applications including RAG pipelines, embedding workflows, vector store integrations, and model serving infrastructure
• Owns observability, evaluation, and safety of production AI systems — including prompt monitoring, output validation, cost tracking, and latency optimization
• Identifies and executes opportunities to automate remediation of recurring issues and improve operational stability
• Executes creative software solutions, including design, development, and technical troubleshooting to solve complex and ambiguous problems
• Mentors and coaches junior and mid-level engineers, conducting code reviews and sharing engineering best practices
• Contributes to firmwide frameworks, tools, and SDLC practices as an engaged member of the engineering community
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Hands-on experience building and shipping LLM-based applications and agentic systems with tool use, memory, and multi-step reasoning in production environments
• Advanced proficiency in one or more programming languages, particularly Python and/or Java
• Deep experience with large-scale data processing, microservices, API design, and event streaming (Kafka)
• Working knowledge of relational and No
  
  SQL databases, vector stores, and data lake architectures
• Experience with caching technologies (Redis, Mem Cached), observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
• Proficiency in CI/CD, test-driven development, automation, and all aspects of the Software Development Lifecycle
• Strong understanding of agile methodologies, application resiliency, and security best practices
• Practical cloud-native engineering experience (AWS, Azure, or GCP)
• Demonstrated experience leading effective…