As a Lead Software Engineer at JPMorganChase with in engineering-intelligence platform 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

• 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
• Develops secure and high-quality production code, and reviews and debugs code written by others

• 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.

• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
• Designs and delivers services across a distributed, multi-tenant platform — backend APIs, data-collection and ingestion workloads, data-pipeline jobs, and the customer-facing dashboard — that must run identically in cloud SaaS and in a customer's own isolated AWS environment
• Champions a security- and privacy-by-design posture (data residency, least-privilege IAM, default-deny egress, signed and verified artifacts) throughout the software development life cycle
• Adds to team culture of diversity, opportunity, inclusion, and respect

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Advanced in one or more programming language(s) — Java,Python and/or JavaScript/TypeScript strongly preferred, given a backend built on Django + FastAPI and a React single-page front end

• 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

• Proficiency in automation and continuous delivery methods
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• In-depth knowledge of the financial services industry and their IT systems
• Practical cloud native experience — designing and operating containerized workloads on Kubernetes (EKS), packaged and released with Helm, and provisioned with infrastructure-as-code (Terraform, Pulumi, or CloudFormation)
• Hands-on experience with core AWS services — compute and container platforms (EKS/ECR), managed data stores (RDS/PostgreSQL, S3), event-driven messaging (SQS, EventBridge), and identity/security primitives (IAM, IRSA, KMS, Secrets Manager)

• Experience building and scaling data pipelines / lakehouse workloads — Databricks, Spark, and Delta Lake — and comfort reasoning about event-driven ingestion at scale
• Experience integrating generative-AI / LLM capabilities into production systems (e.g., AWS Bedrock, a self-hosted model such as vLLM, or provider APIs) with an eye to cost, latency, and data-governance trade-offs
• Familiarity with multi-tenant architecture patterns (tenant isolation, schema-per-tenant, per-tenant provisioning and entitlements)
• Experience delivering software into regulated, isolated, or air-gapped environments — data-residency guarantees, egress allowlisting, and control-plane / data-plane separation
• Supply-chain and platform security practice — image signing/verification (e.g., cosign), offline token validation (JWT/OIDC), and least-privilege access design