Sr. 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 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 responsibilities - Leads 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 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 - Ensures compliance with data privacy and security regulations pertinent to AI/ML solutions - Oversees the end-to-end lifecycle of AI/ML projects, from ideation and development to deployment and maintenance - Manage budgets, allocate resources efficiently, and report on progress and challenges to executive leadership Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 5+ years applied experience designing and managing large-scale AI & ML platforms and supporting systems - Advanced knowledge in software engineering, AI/ML, machine learning operations (MLOps), and data governance - Experience in developing or leading large or cross-functional teams of technologists - Demonstrated prior experience influencing highly matrixed, complex organizations and delivering value at scale - Hands-on practical experience delivering system design, application development, testing, and operational stability - Advanced 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 controls - Experience with hiring, developing, and recognizing talent - Extensive 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 methodologies - Understanding of large language model (LLM) techniques, including agents, planning, reasoning, and other related methods - Expertise 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)