Jobs and Careers

Lead Software Engineer - Applied AI ML Lead

Palo Alto, United Statesfull_timeVerifiedPosted 21 Jul 2026

About the role

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 Enterprise Technology, Infrastructure Platforms 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

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problem.
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.
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.
Own infrastructure capacity optimization solutions and build predictive/prescriptive models to identify capacity risk, performance bottlenecks, and right-sizing opportunities.
Design, develop, and productionize GenAI/agentic AI solutions for automation, decision support, and operational workflows, including LLM/SLM apps such as RAG and summarization/extraction.
Engineer production-grade backend services in Python/Java (REST APIs, microservices, reusable libraries) and own cloud-native data ingestion/processing pipelines for capacity analytics and AI use cases.
Build prompt engineering assets, routing strategies, and guardrails, and implement automated plus human-in-the-loop evaluation to improve quality.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Apply MLOps best practices across experimentation, versioning, CI/CD, deployment, monitoring, and lifecycle management; implement testing/benchmarking and observability; define success metrics/governance with stakeholders; and mentor engineers to uphold high standards.
Own and govern the end-to-end AI/ML optimization strategy—from architecture and engineering standards (quality, lifecycle, observability, secure SDLC) through cross-functional execution with SRE/platform/business—to deliver scalable automation, risk reduction, and measurable enterprise outcomes.
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

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
Advanced in one or more programming language(s)
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
Proficient in all aspects of the Software Development Life Cycle
Strong hands-on data engineering stack: Apache Spark (batch optimization, partitioning, shuffle tuning, reliability), Apache Airflow (DAG design, backfills, alerting, operational reliability, CI patterns), and Apache Iceberg (schema evolution, partition specs, snapshots, compaction).
Proven applied AI/ML and GenAI delivery with measurable impact (RAG, extraction, summarization, ranking/classification, copilots, evalu