This listing doesn't post a salary. Most similar roles pay
$184,937–$223,912.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Sr Lead Software Engineer within the Corporate Technology Data Strategy & Architecture organization, you will join an agile team focused on building secure, stable, and scalable technology products. You will develop high-quality production code for data-intensive applications, lead end-to-end design of complex features, and mentor junior engineers while contributing to firmwide frameworks. Your daily work involves building agentic AI systems with multi-agent workflows, implementing LLM-based applications including RAG pipelines and vector store integrations, and managing observability and safety for production AI. You will utilize Python, Java, Kafka, Redis, and Spark/PySpark within cloud-native environments like AWS, Azure, or GCP. The role addresses complex technical challenges in the financial services industry, specifically focusing on large-scale data processing, microservices, and the integration of responsible, governed AI tools into the software development lifecycle.
Develop secure, high-quality production code for data-intensive applications and platforms.
Lead end-to-end design and implementation of complex software features from requirements through deployment.
Build and maintain agentic AI systems with multi-agent workflows and human-in-the-loop controls.
Implement LLM-based applications including RAG pipelines, embedding workflows, and vector store integrations.
Manage observability, evaluation, and safety for production AI systems including prompt monitoring and cost tracking.
Automate the remediation of recurring issues to improve operational stability.
Mentor junior and mid-level engineers through code reviews and sharing engineering best practices.
Drive adoption and governance of approved AI-assisted engineering practices across teams.
What we're looking for
Formal training or certification on software engineering concepts and 5+ years of 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 NoSQL databases, vector stores, and data lake architectures.
Experience with caching technologies, 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.
Practical cloud-native engineering experience (AWS, Azure, or GCP) and experience with enterprise-authorized AI-assisted software development tools.