Senior Lead Software Engineer - Data / Machine Learning Operations

Senior Lead Software Engineer 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 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 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 and 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 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 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)