Lead Software Engineer - Data Engineering & Applied AI

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Data Engineering & Applied AI Lead Software Engineer at JPMorganChase within the Asset & Wealth Management, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities: - Designing, developing, and delivering AI-enabled platform capabilities that power next-generation data management products (Data Quality, Metadata Management, Lineage, Data Retention and Destruction) - Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team. - Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation. - Hands on design and development of agentic production data platform with full stack application ownership. - AI first mindset in developing code using Claude Code, building agents to make application/platform autonomous. - Develop AI skills, agents, MCP server in support of product capabilities to deliver business value - Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems - Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness. - Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation. - Develops secure high-quality production code, and reviews and debugs code written by others - Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems Required qualifications, capabilities, and skills: - Formal training or certification in Software Engineering and 5+ years applied experience - Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices - Strong knowledge of data architecture and modeling patterns, including dimensional modeling and database design (normalization/denormalization). - Use enterprise-authorized AI-assisted development tools (coding, testing, troubleshooting, documentation) with rigorous validation of outputs for correctness, performance, and security. - Apply and coach responsible AI practices, including data sensitivity, secure input/output handling, and resiliency/security standards. - Own end-to-end delivery of data management products: operate/maintain/modernize existing applications and build greenfield capabilities across UI, APIs, services, integrations, and data pipelines in a federated model. - Design and deliver platform services for metadata, lineage, data contracts, data quality, and retention/destruction via well-defined interfaces and workflows. - Evaluate open-source solutions through rapid POCs with success criteria; lead selection, customization, and enterprise hardening for reliability, security, and operational support. - Build workflow orchestration and policy enforcement services, plus scalable batch/streaming integrations (schema evolution, backfills, error handling, contract-driven interoperability). - Establish production-grade operations and controls (SRE practices, monitoring/on-call, incident response/RCA, auditability, least-privilege, disciplined change management) and deliver governed agentic capabilities (safe tool/function calling, autonomy tiers, access-controlled RAG, evaluation/monitoring, audit trails, rollback/fallback) while leading through influence, design reviews, and mentoring. Preferred qualifications, capabilities, and skills: - Preferred financial services or Wealth Management experience, data governance or data management domain expertise, and experience evaluating and enterprise-hardening open-source software. - Frontend: React or Angular with TypeScript. - Backend: Python, Java, or Kotlin with REST or GraphQL. - Data Engineering/Platform: Spark, Kafka, Airflow, Snowflake, and AWS. - Platform engineering: containers, CI/CD, observability, and controlled release practices. - Applied AI with LLMs: Claude Code, Agent, Skills, RAG, embeddings, prompt or configuration management, evaluation and guardrails, and agent tool or function calling - Bachelor’s degree required. Advanced degree is a plus