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$192,050–$228,950.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
Lead Software Engineer - AI Platform Reliability joins the AI/ML Data Platforms organization within the Reliability Engineering team. This role focuses on designing and implementing solutions to enhance the reliability, scalability, and security of AI/ML platforms and applications. The engineer will build reusable platform services, APIs, SDKs, and libraries to standardize how teams consume model hosting, inference, and managed services. Key responsibilities include managing non-functional requirements like observability, cost optimization, and infrastructure management while establishing reference architectures for operational readiness. The role requires expertise in Python, system design, automated testing, and production software stability. Candidates must demonstrate experience with AI-assisted development tools and responsible AI practices regarding data sensitivity. The work centers on the technical challenge of providing stable, high-quality infrastructure to support generative AI use cases and large-scale machine learning workloads across various business lines.
Design and implement solutions to improve the reliability and scalability of AI/ML platforms.
Develop secure, high-quality production code for AI Foundation Services components.
Build reusable platform services, APIs, SDKs, and libraries to standardize how teams consume AI models.
Partner with business units to implement generative AI capabilities from technical design through launch.
Manage non-functional requirements including observability, security controls, infrastructure management, and cost optimization.
Establish and enforce standards for reliability, automation, and operational readiness across services.
Participate in on-call rotations to debug complex production issues and drive durable remediation.
Mentor engineers and improve engineering quality, documentation, and operational rigor.
What we're looking for
Formal training or certification on software engineering concepts and 5+ years of applied experience.
Strong hands-on coding experience in Python for delivering production-grade services.
Experience leading the use of approved AI-assisted software development tools while ensuring output correctness, performance, and security.
Understanding of responsible AI use, including data sensitivity, secure handling of inputs/outputs, and coaching others on compliant adoption.
Hands-on experience with system design, automated testing, debugging, and operational stability for production software.
Experience implementing observability, logging, metrics, alerts, SLOs, incident response, and root-cause analysis for services in production.
Working knowledge of cloud platforms, artificial intelligence, machine learning platforms, distributed systems, or infrastructure engineering.
Experience supporting AI/ML platform capabilities, managing AI infrastructure on cloud platforms, or building reusable "golden path" assets (preferred).
Python
AI Infrastructure
Machine Learning
Cloud Platforms
Distributed Systems
SRE
Observability
System Design
SDKs
Automated Testing
Infrastructure Engineering
Generative AI
Site Reliability Engineering
Python
AI
Machine Learning
CI/CD
Incident Management
Root Cause Analysis
Distributed Systems
SDLC
System Architecture
Scalability
Security