Lead Software Engineer - Platform Services

JPMorganChase•Houston, TX

About The Position

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Corporate Technology - CTIM Platform Services 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. You will lead delivery of high-quality technical solutions, drive roadmaps and agile execution, mentor engineers, and implement AI-enabled engineering capabilities, including tool functions/skills and MCP servers/agents within governance, security, and risk controls.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Strong proficiency in Java and strong working experience with Python.
Strong architecture and design skills across systems with sound engineering judgment.
Strong engineering fundamentals (OOD, design patterns, SOLID) and a quality-first mindset (testing/TDD).
Strong integration and data fundamentals (SQL/PL-SQL; event messaging such as MQ/Kafka).
Experience operating production services with CI/CD, observability, and reliability best practices.
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 practice.
Ability to create roadmaps/milestones, manage dependencies, and execute effectively in agile/scrum environments.
Track record of mentoring engineers, leading reviews, and raising engineering standards across a team.
Strong communication and collaboration skills across distributed, cross-functional stakeholders.

Nice To Haves

ServiceNow experience (including familiarity with the ServiceNow data model).
Experience using AI-assisted development tools responsibly with strong engineering judgment and secure coding practices.
Experience with MCP operations patterns (authN/Z, logging/traceability, rate limiting, versioning, safe rollout).
Experience designing AI agents with grounding/RAG patterns, deterministic fallbacks, prompt/version management, and eval harnesses.
Understanding of data privacy/security considerations for agent workflows (data minimization, secrets management, least privilege, auditability).
Experience in AWS or other public cloud platforms.

Responsibilities

Lead end-to-end design, implementation, and operation of complex platform features, integrations, and production support/operational resilience for Corporate Technology applications across UNIX/Linux/Windows / Public and Private Cloud platforms ensuring stable processing and high availability.
Have strong understanding of Incident, Problem, Change, and Capacity Management —to assist with driving prioritization, stakeholder communications, escalations, post-incident reviews, and continuous improvement to prevent recurrence and improve SLA outcomes.
Provide hands-on troubleshooting across batch processing, UNIX/Linux file systems, data stores, access/core services ( SFTP/SSH/LDAP ), and scheduling ( Autosys/Crontab ); optimize job flows, reduce runtimes, and strengthen recovery/re-run procedures.
Deliver data solutions to underpin reliable outcomes—controlled data loads/reconciliations/reporting/alerting and data quality remediation for databases across Sybase/Oracle/Informix/MS SQL (procedures/packages/views/triggers, indexing/statistics, fragmentation, replication such as Sybase Replication ).
Build and operate automation and observability —Perl and shell scripting ( KSH/Bash; sed/awk ) to streamline monitoring, provisioning, and runbooks; enhance metrics/alerting, dashboards, and automated reporting to surface risks early, improve service level performance and operational runbooks.
Develop and operationalize AI/agent capabilities —create assistant skills/tool functions with clear interfaces, schemas, validation, error handling, and observability; stand up and operate MCP (Model Context Protocol) servers/integrations and reusable agent patterns to accelerate delivery.
Establish evaluation checks, guardrails, monitoring, and validation standards so AI behavior is accurate, safe, and verifiable; drive data readiness and integration practices (modeling, pipeline alignment, quality controls) that improve AI and platform reliability.
Uphold strong SDLC rigor and predictable delivery —technical roadmaps/milestones, secure coding, code reviews, automated testing, CI/CD, and production readiness; partner with product and analyst stakeholders on estimates, dependencies, delivery plans, and outcomes.
Manage data operations—controlled ad hoc data loads , reconciliations, client reporting, and resolution of day-to-day data quality/processing issues.
Provide technical leadership and mentoring —delegate effectively, unblock delivery, scale team effectiveness via documentation/knowledge management (runbooks, known error database, training), and drive adoption of enterprise-authorized AI-assisted engineering practices (e.g., refactoring/review, test acceleration, incident/RCA support) with reuse of proven patterns across the team.