About the role
from listingDesign and deliver market-leading technology products in a secure and scalable way as a seasoned member of an agile team
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a 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
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.
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
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 and proficient years 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
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.
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
Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds and Overall knowledge of the Software Development Life Cycle
Preferred qualifications, capabilities, and skills
Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector
Experience working on 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