Sr Lead Software Engineer

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Category:
Software Engineering

Job Schedule:

Full time

Posted Date: T19:36:26+00:00

Job Shift:

Base Pay/Salary:
Jersey City,NJ $-$

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.

Job responsibilities

* 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.

Required qualifications, capabilities, and skills

* 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 use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for…