Lead Software Engineer-PYTHON

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As a Lead Software Engineer at JPMorganChase within the Enterprise Technology, Consumer & Community Banking risk technology team, 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

• Designs, codes, tests, and delivers automation (including LLMs/agents) to eliminate manual operational work and streamline AO workstreams’ remediations (e.g., control items, security vulnerabilities, upgrades, and FARM findings), applying strong knowledge of tools across the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation.

• Governs application risk, controls, and compliance by owning adherence to firm standards, partnering with Technology Risk & Controls, managing Technology Lifecycle Management (TLM), and driving closure of issues/findings (e.g., FARM) through effective remediation and evidence management.

• 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.

• Owns security and data accountability for the application by ensuring strong authentication/authorization, vulnerability and certificate hygiene, and proper data registration/classification plus compliant storage/retention/disposal.

• 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.

• Coordinates across product and engineering at scale to prioritize and drive execution with a clear sense of urgency for AO workstreams, including influencing/coaching teams and aligning execution across large developer communities, while documenting and sharing knowledge via internal forums and communities of practice and promoting reuse of effective patterns across the team.

• Runs resilient, well-operated production services end-to-end by implementing monitoring/logging and anomaly detection, maintaining secure network configurations/least privilege, and leading/supporting incident/problem/change management and recovery/resiliency readiness; demonstrates and champions site reliability culture and practices, leads initiatives to improve reliability/stability using data-driven analytics to improve service levels, collaborates to define service level indicators and establish reasonable service level objectives and error budgets with customers, and drives team adoption of enterprise-authorized AI-assisted engineering practices (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support) with consistent validation standards (secure coding, peer review, automated testing).

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience ( NAMR/APAC – India/ LATAM/ Hong Kong)

• Proven track record designing, coding, testing, and delivering production-grade software in at least one technology stack, with strong foundations in software engineering processes and the ability to solve complex data structures/algorithms problems.

• Advanced development experience in Java or Python, with excellent debugging and troubleshooting skills for complex production issues.

• Experience building scalable data processing, Big Data, and ETL pipelines (e.g., Hortonworks and/or AWS-based solutions).

• Working knowledge of core infrastructure (routers, load balancers, compute, storage, networks, cloud products) and the ability to troubleshoot common networking issues.

• Deep proficiency in reliability, scalability, performance, security, enterprise architecture, and toil reduction, with the ability to implement SRE practices and operate strong observability (white/black box monitoring, telemetry, SLOs/alerting) using tools such as Grafana, Dynatrace, Prometheus, Datadog, and Splunk.

• Demonstrated ability to learn/evaluate new technologies, teach and mentor others, collaborate across stakeholder groups, and lead responsible use of approved AI-assisted engineering tools (coding, review, testing, troubleshooting) with clear team expectations for validating AI outputs (correctness, performance, security) and for secure handling of sensitive data in inputs/outputs.

• 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

Preferred qualifications, capabilities, and skills

• Current AWS credential (e.g., Cloud Practitioner, Solutions Architect, SysOps Administrator, or Developer) demonstrating working knowledge of core AWS services, cloud architecture patterns, security and identity fundamentals (IAM), networking basics (VPC), monitoring/logging, and cost-awareness best practices.

• Recognized automation/DevOps credential (e.g., Kubernetes/containers, CI/CD, Infrastructure as Code, or site reliability)

• Hands-on experience with AWS and/or other cloud platforms, plus containerization and orchestration technologies (e.g., Docker, Kubernetes, ECS).