We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You will work on challenging Cloud-native data, backend engineering and AIML engineering, helping us industrialize AI/ML models at Production scale. This role is a technical hands-on Engineering role. Experience with data science/ML modeling is advantageous but not essential to this role.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps
- Designs and implements data engineering solutions, leveraging modern big data technologies
- 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.
- Contributes to software engineering communities of practice and events that explore new and emerging technologies
- Embraces a passion for learning, problem-solving, creative thinking and a can-do attitude.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and proficient applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Proficient in coding in one or more languages- Python
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages. Overall knowledge of the Software Development Life Cycle
- Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications
- Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies
- Practical experience developing Production-scale Cloud-native data engineering solutions in commercial environments
- Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack
- 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.
- Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds
Preferred qualifications, capabilities, and skills
- Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector
- Experience working on recommendation systems, LLM applications or other AI/ML systems
- Practical experience with Kubernetes, EKS, Docker, MLOps
- Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases
- Prior experience collaborating with data scientists
Skills Required
- Formal training or certification on software engineering concepts and proficient applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Proficient in coding in one or more languages - Python
- Experience developing, debugging, and maintaining code in a large corporate environment with modern programming and database querying languages
- Overall knowledge of the Software Development Life Cycle
- Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications
- Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies
- Practical experience developing production-scale Cloud-native data engineering solutions in commercial environments
- Familiarity with Cloud data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools and setting validation expectations
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs/outputs
- Ability to convey design choices and results clearly and communicate effectively to stakeholders
- Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector
- Experience working on recommendation systems, LLM applications or other AI/ML systems
- Practical experience with Kubernetes, EKS, Docker, MLOps
- Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases
- Prior experience collaborating with data scientists
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental health coverage are comprehensive, with on-site clinics, preventive care, and specialized supports such as maternity nurse guidance and fertility treatments. Wellness activities can help offset copays and out-of-pocket costs, reinforcing the perceived strength of health benefits.
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Retirement Support — A 401(k) with dollar-for-dollar matching and additional automatic pay credits reflect strong employer-backed retirement savings. An employee stock purchase plan and related financial programs further bolster long-term financial support.
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Leave & Time Off Breadth — Paid time off, sick time, holidays, and generous parental leave are provided alongside family medical leave and adoption/fertility assistance. Additional programs like caregiver support and volunteer time off expand the breadth of time-away options.
JPMorganChase Insights
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
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