JPMorganChase
Website:
jpmorganchase.com
Company:
https://www.linkedin.com/company/jpmorganchase
Seniority: Not Applicable
Industries: Financial Services
Job details:
Job Description
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 JPMorganChase within the Employee Platforms 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, 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.
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- Serves as a function-wide subject matter expert in one or more areas of focus
- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
Required Qualifications, Capabilities, And Skills
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Hands-on practical experience in Python, SQL, Databricks, Knowledge Graphs in Production
- Advanced in one or more programming language(s) like Python
- 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.
- Highly proficient in coding in one or more languages such as Python, SQL, Java and R programming languages Experience with one or more platform tech stacks such as AWS, Docker, Kubernetes, Data bricks and CI/CD pipelines.
- Solid understanding of using ML techniques specially in Natural Language Processing (NLP), Knowledge Graph and Large Language Models (LLMs)
- Proficient in all aspects of the SDLC and ADLC
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Proficiency in optimizing and tuning AI models to ensure efficient, scalable solutions, with experience in building and deploying ML models on cloud platforms such as AWS and using tools like Sagemaker and EKS
Preferred Qualifications, Capabilities, And Skills
- Knowledge of the financial services industry and their IT systems
- Cloud native experience -AWS
- Knowledge of data engineering practices to support AI model training and deployment, along with a strong understanding of machine learning algorithms and techniques—including supervised, unsupervised, and reinforcement learning—and hands-on experience with libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras
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