Data

JPMorgan Chase & Co.•Houston, TX

About The Position

As a Lead Software Engineer on the engineering-intelligence platform at JPMorgan Chase, you will be an integral part of an agile team focused on enhancing, building, and delivering market-leading technology products securely, stably, and scalably. You will act as a core technical contributor, responsible for developing critical technology solutions across multiple technical areas within various business functions to support the firm's business objectives.

Requirements

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 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).

Nice To Haves

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.

Responsibilities

Execute creative software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems.
Develop secure and high-quality production code, and review and debug code written by others.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards and promoting reuse of effective patterns.
Apply knowledge of tools within the Software Development Life Cycle toolchain, including AI-assisted development and automation capabilities, to improve value realized by automation.
Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Lead evaluation sessions with external vendors, startups, and internal teams to probe architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
Lead communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
Design and deliver services across a distributed, multi-tenant platform, including backend APIs, data-collection and ingestion workloads, data-pipeline jobs, and customer-facing dashboards, ensuring identical functionality in cloud SaaS and isolated customer AWS environments.
Champion a security- and privacy-by-design posture throughout the software development life cycle.
Contribute to a team culture of diversity, opportunity, inclusion, and respect.