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
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives adoption and governance of approved AI assisted engineering practices across teams to improve code quality, deliver speed, and operation 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 patters 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
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Design and develop large-scale solutions or platforms using Cloud services (i.e. AWS) in alignment with the firm wide strategies and security controls
- Deploy and enable cloud based solutions at firm level, supporting complex analytics and day to day business operations
- Migrate legacy an big data applications at Cloud native applications with zero downtime
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- Develop solutions or tools to monitor, provision components for automation or the processes, services, and reports
- Influences peers and project decision-makers to consider the use and application of leading-edge technologies
- Adds to the team culture of diversity, opportunity, inclusion, and respect
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 delivering system design, application development, testing, and operational stability
- Drives adoption and governance of approved AI assisted engineering practices across teams to improve code quality, deliver speed, and operation 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 patters 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
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Good knowledge of Machine Learning modelling as an engineer
- Advanced in one or more programming language(s) and framework(s) (i.e., Python, Java, Big Data, Data pipeline, Machine Learning, etc.)
- Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Advanced knowledge of application, data, and infrastructure architecture disciplines, and working in software development, OOPS and SDLC
- Ability to tackle design and functionality problems independently with little to no oversight
- Practical cloud native experience
Preferred qualifications, capabilities, and skills
- AWS certifications (e.g. Solutions Architect Associate)
- Knowledge of RAG architectures and exposure to AI/Automation technologies that improve operations
- Experience with building Data Pipelines in Spark, Tuning Spark queries
- Understands Python Machine Learning libraries and ecosystems (i.e., Pandas, Numpy, etc.)
- Working knowledge with Big Data platforms (i.e., Hadoop preferred)
- Experience in Cloud Technologies (i.e., AWS - Databricks preferred)