JPMorgan Chase
From JPMorgan Chase's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
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 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 10+ 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 NoSQL databases, vector stores, and data lake architectures
Experience with caching technologies (Redis, MemCached), 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 correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Experience with LLM orchestration frameworks . Hands-on experience with model serving infrastructure or managed endpoints (AWS Bedrock, Azure OpenAI)
Familiarity with AI evaluation and observability practices: evals frameworks, red-teaming, prompt drift detection, and cost/latency monitoring.Understanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflows
Experience with modern data platforms such as Databricks or Snowflake.Hands-on experience with Spark/PySpark and big data processing at scale
Knowledge of the financial services industry and its technology system.
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