Lead Software Engineer, AI Foundation Services

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Plano, TXPalo Alto, CASeattle, WAJersey City, NJWilmington, DEMcLean, VA
Employment: Full-time
Posted: 105 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $178k

$126k $238k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $142,400–$213,500.

Based on 240 similar postings.

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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 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, AI Foundation Services

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Lead Software Engineer-AI Foundation Services joins the AIML Data Platforms team within Chief Data and Analytics to build AI foundation services for GenAI and ML at enterprise scale. This role involves leading the hands-on delivery of secure, cloud-native platform capabilities, including reusable APIs, SDKs, and libraries that standardize model hosting and inference. You will translate functional requirements into technical designs, develop production-grade code, and create engineering assets like reference implementations and runbooks. Key technologies include Python, Java, or Go, alongside infrastructure-as-code tools like Terraform, Kubernetes, and CI/CD pipelines. You will solve technical problems related to model serving patterns, GPU-enabled platforms, and AI workload optimization while ensuring secure, stable, and scalable software components through automated testing, observability, and responsible AI engineering practices.

Skills

Python Java Go Kubernetes Terraform CI/CD infrastructure-as-code Cloud-native APIs SDKs Distributed Systems Machine Learning System Design Automated Testing Observability Logging Metrics Service Level Objectives Root-cause Analysis

What you'll do

Implement AI Foundation Services capabilities to unblock GenAI and AI use cases for various lines of business.
Build and enhance reusable platform services, APIs, SDKs, and libraries for model hosting and inference.
Translate functional requirements into technical designs, engineering tasks, and delivery milestones.
Develop secure, high-quality production code and perform code reviews, debugging, and defect remediation.
Create engineering assets like reference implementations, runbooks, and onboarding guides to accelerate team adoption.
Design and implement scalable software components using cloud-native practices and platform engineering standards.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Communicate technical progress, risks, and implementation options to engineering managers and senior stakeholders.

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, Java, or Go to deliver production-grade services or APIs.
Experience building shared services, reusable components, or platform capabilities for multiple application or engineering teams.
Experience with infrastructure-as-code and cloud-native delivery practices including Terraform, Kubernetes, and CI/CD pipelines.
Demonstrated experience leading the use of approved AI-assisted software development tools and validating AI outputs for security and performance.
Strong understanding of responsible AI use, data sensitivity, and coaching engineers on safe, compliant adoption within delivery practices.
Hands-on experience with system design, automated testing, debugging, and production software operational stability.
Experience implementing observability, logging, metrics, alerts, and incident response practices for production services.
Experience supporting AI/ML or GenAI platform capabilities, including model hosting, inference services, or managed AI services (preferred).
Experience with GPU-enabled platforms or AI workload optimization, including inference latency and capacity planning (preferred).
Experience building reusable "golden path" assets such as templates, SDKs, and onboarding guides (preferred).
Familiarity with model serving patterns, safety controls, authorization, and rate limiting (preferred).

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