Lead Software Engineer- AWS Infra/Terraform expert - Data

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 Job responsibilities 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 Required qualifications, capabilities, and skills 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 ) Preferred qualifications, capabilities, and skills 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