JP Morgan Chase

Runs a consumer bank and an investment bank

Lead Software Engineer - Java/Python/Spring Boot/Microservices

AI Application Engineering +1Updated on Nov 10

Full-timeNo salary listedIn-person

About the Job

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
At least 10 years of recent hands-on software development experience in large-scale distributed systems, primarily using Python, Java/J2EE, and modern Java/Spring Boot microservices.
At least 3 years of experience developing in Linux environments.
Demonstrated experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, or troubleshooting, and setting expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption.
Strong experience with REST APIs and service-oriented or microservices architecture.
Strong Kubernetes orchestration experience building, deploying, and operating production services.
Messaging expertise with Kafka, MQ, or similar platforms.
Experience with backend infrastructure patterns such as load balancing and autoscaling.
Experience with log analytics and observability tools such as ELK or Splunk.
Exposure to AI/ML platforms, including MLOps, feature engineering, model hosting or operationalization, and AWS and/or hybrid on-premises and cloud environments.

Responsibilities

Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, including code review and refactoring, test strategy acceleration, and incident or root-cause analysis support; establish validation standards for secure coding, peer review, and automated testing, and promote reuse of effective patterns.
Apply software development life cycle toolchain knowledge, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Architect and implement resilient, highly scalable, fault-tolerant, low-latency services and drive target-state architecture.
Design and deploy services that integrate with enterprise systems, ensuring functional, performance, scalability, security, governance, and auditability requirements are met.
Lead and mentor the development team in a high-pressure delivery environment, manage multiple deliverables across business groups, and strengthen stakeholder relationships.
Collaborate with line-of-business users, subject-matter experts, architects, database administrators, and system administrators to design solutions, manage enhancements, and resolve issues.
Build and mature capabilities to execute machine-learning pipelines for fraud detection and risk assessment, and support modeling teams with implementation and tooling.
Productionize models built by data scientists, including validation readiness and quality controls before live usage.
Design and own reusable machine-learning platform components, such as feature-store patterns and delivery pipelines, and establish monitoring and alerting for performance, scalability, availability, and reliability.
Build agentic AI services to automate and enhance engineering and model-operations workflows, including tool-using agents, orchestration, state management, and audit-ready traceability.
Define and implement guardrails and evaluation approaches for production agentic AI, covering quality, safety, latency, and cost.

Desired Qualifications

Strong communication skills and the ability to influence senior technology and business stakeholders.
Strong software development life cycle knowledge and agile working practices, including continuous integration and continuous delivery, application resiliency, security, testing, and operational stability.
Experience building and operating large language model-driven agents with tool integration, monitoring or telemetry, and governance or audit considerations.
Experience with NoSQL databases such as Cassandra.
Experience implementing BERT or Transformer models.

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Corporate Sector Technology, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities:

Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Architect and implement resilient, highly scalable, fault-tolerant, low-latency services and drive target-state architecture.
Design and deploy services that integrate with enterprise systems; ensure functional, performance, scalability, security, governance, and auditability requirements are met.
Lead and mentor the development team in a high-pressured delivery environment; manage multiple deliverables across business groups and strengthen stakeholder relationships.
Collaborate with LOB users, SMEs, architects, DBAs, and system administrators to design solutions, manage enhancements, and resolve issues.
Build and mature capabilities that execute ML pipelines for fraud detection and risk assessment; support modeling teams in implementation and tooling.
Production Alize models built by data scientists, including validation readiness and quality controls prior to live usage.
Design and own reusable ML platform components (e.g., feature-store patterns, delivery pipelines) and establish monitoring/alerting for performance, scalability, availability, and reliability.
Build agentic AI services to automate and enhance engineering and model-ops workflows (tool-using agents, orchestration, state management, and audit-ready traceability).
Define and implement guardrails and evaluation approaches for agentic AI in production (quality, safety, latency, and cost).

Required qualifications, capabilities, and skills:

Formal training or certification on software engineering concepts and 5+ years applied experience
10+ years of recent hands-on software development experience in large-scale distributed systems, primarily Python/Java/J2EE and modern Java/Spring Boot microservices.
3+ years of experience developing in Linux environments.
Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
Strong experience with REST APIs and service-oriented / microservices architecture.
Strong Kubernetes orchestration experience (building, deploying, and operating production services).
Messaging expertise with Kafka, MQ, or similar platforms.
Experience with backend infrastructure patterns (e.g., load balancing, autoscaling).
Experience with log analytics / observability tools (e.g., ELK, Splunk).
AI/ML platform exposure (MLOps, feature engineering, model hosting/operationalization; AWS and/or hybrid on-prem + cloud).

Preferred qualifications, capabilities, and skills:

Strong communication skills and proven ability to influence across senior technology and business stakeholders.
Strong SDLC knowledge and agile ways of working, including CI/CD, application resiliency, security, testing, and operational stability.
Agentic AI experience preferred: building and operating LLM-driven agents with tool integration, monitoring/telemetry, and governance/audit considerations.
Experience with NoSQL databases such as Cassandra (preferred).
Experience with BERT, Transformer Model implementation

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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