Lead Software Engineer- Python & AWS

Job Description

As a Lead Cloud Software Engineer at JPMorgan

Chase within the Cloud Financial Management organization, you design and build modern, cloud‑native software solutions with a strong focus on cost optimization. This role is hands‑on and requires advanced engineering skills to develop scalable services, data pipelines, and automation that improve cloud efficiency, governance, and performance. You will also apply AI skills and develop/manage AI agents that enable natural‑language querying and analysis of large‑scale cloud financial data.

You will collaborate across teams to deliver best‑in‑class outcomes and accelerate cloud adoption throughout the firm.

• Design, build, test, deploy, and maintain cloud‑native AWS solutions (S3, EC2, Lambda, Glue, Step Functions) focused on cost efficiency and optimized resource usage.
• Define and maintain engineering/architecture standards, cloud resource management practices, governance frameworks, and security/compliance controls.
• Build and operate reliable pipelines that enable cost optimization, data flow, and scalable processing.
• Develop an optimization and recommendation engine, including AI agents and AI‑driven workflow automation for large‑scale processing and cost improvements.
• Partner with Product Managers and domain leads to understand needs, provide best‑practice guidance, and deliver training/support to domain teams.
• Monitor and optimize performance, reliability, and scalability; write/analyze Splunk queries on AWS Cloud Trail to support monitoring, alerting, and security visibility.
• Use SDLC toolchain capabilities, including enterprise‑authorized AI‑assisted development and modern coding productivity tools, and stay current on cloud/software engineering trends.
• Promote a culture of diversity, opportunity, inclusion, and respect.

• Proficiency in Python and SQL, able to write maintainable code for data processing and analysis (experience with performance/scaling is a plus).
• Working knowledge of AWS and cloud‑native design, with hands‑on experience in several services such as S3, EC2, ECS, Lambda, Glue (depth in all is not required).
• Experience using enterprise‑approved AI‑assisted development tools for coding and delivery tasks (e.g., code review, testing, troubleshooting), with sound judgment on when/how to validate AI‑generated outputs.
• Understanding of responsible AI use in engineering workflows, including secure handling of inputs/outputs and awareness of data sensitivity, resiliency, and security expectations.
• Familiarity with Data Lake / Big Data concepts and technologies; experience working with large datasets is preferred.
• Experience with infrastructure as code (Terraform preferred; similar tools acceptable) for provisioning and managing cloud resources.
• Ability to contribute to resilient systems with solid engineering fundamentals (code quality, testing, and CI/CD); deep expertise in all areas is not mandatory.
• Exposure to workflow automation and/or AI agent patterns that can query and analyze data securely (hands‑on experience preferred, openness to learn acceptable).
• Experience building RESTful APIs and microservices; familiarity with React/Type Script is a plus but not required.
• Ability to write or learn Splunk queries; prior experience analyzing AWS Cloud Trail logs is preferred.
• Nice to have: experience with cloud cost optimization strategies, and building pipelines/engines for resource efficiency.