Sr. Lead Software Engineer

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 Sr. Lead Software Engineer at JPMorgan Chase within the Consumer and Communicate Banking - Data Technology Data Platform 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 responsibilitiesLeads the design and development of our AI/ML platform, ensuring robustness, scalability, and high performance.Manages and grows a team of talented engineers and AI specialists, fostering a culture of innovation and continuous improvement.Collaborates with product management and cross-functional teams to define product vision, roadmaps, and deliverables for AI-driven features and capabilities.Drives the adoption of best practices in software engineering, machine learning operations (MLOps), and data governance.Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendorsDevelops secure and high-quality production code, and reviews and debugs code written by othersDrives 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 toolchainApplies 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 scaleEnsures compliance with data privacy and security regulations pertinent to AI/ML solutionsOversees the end-to-end lifecycle of AI/ML projects, from ideation and development to deployment and maintenanceManage budgets, allocate resources efficiently, and report on progress and challenges to executive leadership Required qualifications, capabilities, and skillsFormal training or certification on software engineering concepts and 5+ years applied experience designing and managing large-scale AI & ML platforms and supporting systemsAdvanced knowledge in software engineering, AI/ML, machine learning operations (MLOps), and data governance Experience in developing or leading large or cross-functional teams of technologistsDemonstrated prior experience influencing highly matrixed, complex organizations and delivering value at scaleHands-on practical experience delivering system design, application development, testing, and operational stabilityAdvanced in one or more programming language(s)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 controlsExperience with hiring, developing, and recognizing talentExtensive practical cloud-native experience Preferred qualifications, capabilities, and skills In-depth understanding of search/ranking, recommender systems, RAG (similarity search), graph techniques, and other advanced methodologiesUnderstanding of large language model (LLM) techniques, including agents, planning, reasoning, and other related methodsExpertise in training and fine-tuning large language models (LLMs) and embedding models, including advanced knowledge in the areas of LLM operations (LLM Ops) and AI operations (AIOps)