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Sr Lead Software Engineer- Cloud Infrastructure (AWS + Full-Stack)

JPMorgan Chase
Houston, USA
Full-time
Senior · 5+ years experience
Onsite
Discovered 2 days ago
awsbedrockcloudformationdatabricksdelta-lakedjango

Full Job Posting

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
  • 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

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