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 - Python/AWS/AI/LLM at JPMorganChase within the Asset Wealth Management 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 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
- Collaborate with firmwide AI/ML teams, Business and Product Partners, peers in geographically dispersed teams, and colleagues across JPMorgan AWM’s lines of business and functions to drive alignment, accelerate adoption of common AI capabilities, and deliver impactful solutions
- Hands-on architecture and implementation of lighthouse ML and LLM-powered solutions
- Design and implement highly scalable and reliable data processing pipelines and deploy model inference services
- Experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a huge technology and business impact; Deploy solutions into public cloud infrastructure.
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- 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.
- Advanced in one or more programming language(s)
- 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.)Experience in using LLMs (OpenAI, Anthropic, or other models) to solve business problems, including full workflow toolset such as tracing, evaluations, and guardrails;
- Must have strong programming skills in Python; Deep knowledge in Data Structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, and Statistics
- Demonstrated ability to perform independent end-to-end solution design, with attention on modern architecture, scalability and security
- Expert knowledge of AWS cloud computing platforms
- Knowledge of data management, including data model design, as well as real-time data processing on both SQL (such as Postgres) and NoSQL stores (such as OpenSearch and Redis)
- Proven leadership capacity, including new AI/ML idea generation and GenAI-based solutions; Excellent communication skills and ability to communicate with senior technical and business partners
Preferred qualifications, capabilities, and skills
- Expert knowledge of one of the cloud computing platforms preferred: Amazon Web Services (AWS), Azure, Kubernetes
- Emerging technologies