About the role
from listingBuild and deliver critical solutions across the Engineering-intelligence platform as an integral part of an agile team.
As a Senior Lead Software Engineer at JPMorganChase with in Corp Sector, you are part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across a variety of technologies to support one or more of the firm's portfolios.
Job responsibilities
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 decisions that influence the product design, application functionality, and technical operations and processes
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.
Serves as a function-wide subject matter expert in one or more areas of focus
Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
Influences peers and project decision-makers to consider the use and application of leading-edge technologies
Owns the end-to-end architecture of a distributed, multi-tenant platform that must run identically in cloud SaaS and in a customer's own isolated AWS environment — backend APIs, data-collection and ingestion workloads, data-pipeline jobs, and the customer-facing dashboard
Establishes the security- and privacy-by-design bar for the platform (data residency, control-plane / data-plane separation, least-privilege IAM, default-deny egress, signed and verified artifacts) and holds the organization to it
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, with demonstrated experience leading and mentoring technologists
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 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.
Advanced proficiency in automation and continuous delivery methods, and in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Ability to tackle design and functionality problems independently with little to no oversight
Practical cloud native experience at an architectural level — designing and operating containerized workloads on Kubernetes (EKS) , packaged and released with Helm , and provisioned with infrastructure-as-code ( Terraform , Pulumi, or CloudFormation)
Deep, 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 )
In-depth knowledge of the financial services industry and their IT systems
Demonstrated proficiency across a technical discipline such as cloud, artificial intelligence, machine learning, or large-scale data engineering, with a track record of setting technical direction for others
Preferred qualifications, capabilities, and skills
Proven experience architecting and scaling data pipelines / lakehouse platforms — Databricks, Spark, and Delta Lake — and designing event-driven ingestion for high-volume, multi-tenant workloads
Depth in production generative-AI / LLM systems (e.g., AWS Bedrock, self-hosted models such as vLLM, or provider APIs), including the cost, latency, evaluation, and data-governance trade-offs of running inference inside a customer's VPC
Experience defining multi-tenant architecture standards — tenant isolation, schema-per-tenant, per-tenant provisioning, and feature entitlements
Track record delivering software into regulated, isolated, or air-gapped environments — data-residency guarantees, egress allowlisting, and control-plane / data-plane separation as an established design pattern