JPMorgan Chase
Principal Software Engineer - AI Foundations
$204K–$285K
Jersey City, NJ +3 · Principal · Full-time · Posted Jul 16, 2026
- Location
- Jersey City, NJ
Palo Alto, CA
Seattle, WA
New York, NY - Experience
- 7+ years applied experience.
- Education
- Formal training or certification on software engineering concepts.
About the job
Builds and operates AI and ML platform capabilities, including AI-enabled applications, MLOps, model training workflows, and agentic AI development practices across teams.
What you'll do
- Design, build, troubleshoot, and support AI-enabled applications, services, SDK integrations, and reusable platform components.
- Implement end-to-end MLOps and training capabilities, including versioning, reproducible pipelines, deployment, and continuous evaluation.
- Set guardrails and promote secure, reliable agentic AI-enabled engineering workflows across teams.
What they ask for
- Formal training or certification in software engineering concepts and 7+ years of applied experience.
- Hands-on experience delivering system design, application development, testing, and operational stability for large-scale platforms and services.
- Expert proficiency in one or more programming languages, with strong code quality, testing, and debugging practices.
- Experience designing and leading adoption of agentic AI-enabled development practices, including human-in-the-loop validation, auditability, and secure data handling.
Nice to have
- Practical experience with distributed compute and scalable model training or fine-tuning, such as Ray or comparable frameworks.
- Experience building LLM or agentic-system development platforms, including fine-tuning, evaluation, and retrieval or tooling integration.
Pay
USD 204,250.00-$285,000.00 base salary (Jersey City, NJ); USD 204,250.00-$285,000.00 base salary (Palo Alto, CA); USD 204,250.00-$285,000.00 base salary (Seattle, WA); USD 204,250.00-$285,000.00 base salary (New York, NY)
Tools
Python, Java, Scala, Go, Ray, SDK, REST, CI/CD
AI focus
Develops AI-enabled applications and services, MLOps and model training capabilities, LLM evaluation, and agentic AI engineering workflows with validation, security, and governance guardrails.
Model-written summary · the original posting is authoritative