About the role
from listingDrive significant business impact and tackle a diverse array of challenges that span multiple technologies and applications
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 Consumer and Community Banking - Risk Technology Portfolio team, 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:
Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown 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 enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
Drive the AI / ML delivery best practices along with software engineering best practices.
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.
Define architecture for series of complex deliverables.
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Required qualifications, capabilities, and skills:
Formal training and certification on software engineering concepts and 5+ years applied experience. In addition, 2+ years of experience leading technologists to manage and solve complex technical items within your domain of expertise
Hands-on practical experience in system design, application development, testing, and operational stability
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Good knowledge of Machine Learning modelling as an engineer
Experience in working with one or more programming language(s) and framework(s) (i.e., Python, PySpark, Big Data, Data pipeline, Machine Learning, etc.)
Strong SQL skills with ability to validate features and data quality at scale
Experience configuring and optimizing Spark clusters for processing large data.
Hands-on experience working with AWS services including EMR, EC2, S3, and CloudWatch
Working knowledge on Databricks in creating Data Pipelines, Machine learning models deployment.
Preferred qualifications, skills, and capabilities:
Expertise with programming languages like Java, python
Experience in Cloud Technologies (i.e., AWS - Databricks preferred)
Experience with serving high volume, High availability & ultra low latency API solutions with Java - Spring Boot applications
Awareness with Python Machine Learning libraries and ecosystems (i.e., Pandas, Numpy, etc.)
AWS certifications (e.g. Solutions Architect Associate)
Exposure to workflow orchestration tools (e.g. Apache Airflow)
Familiarity with modern LLM techniques