Principal Software Engineer AI Foundations

If you are looking for a game-changing career working for one of the worlds leading financial institutionsyouvecome to the right place.

As a Principal Software Engineer atJPMorganChasewithin theChief Data and Analytics Office (CDAO)you provide expertise and engineering excellence as an integral part of an agile team to enhance build and deliver trusted market-leading technology products in a secure stable and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firms portfolios.

Job Responsibilities

• Design build and troubleshoot AI-enabled applications and AI services delivering creative scalable solutions.
• Develop secure high-quality production code; review debug and improve code written by others.
• Own and support SDK and service integrations ensuring reliability performance and maintainability.
• Build and ship AI-powered features including prompt design function calling and SDK/REST integrations (no prior experiencerequired).
• Design and implement end-to-endMLOpscapabilitiesincludingdata/model versioning reproducible training pipelines CI/CD for models deployment patterns and continuous evaluation/monitoring.
• Contribute to next-generation training techniques (distributed fine-tuning RLHF/DPO-style workflows synthetic data generation and automated evaluation) and productize them into reusable platform primitives.
• Identifyrecurring issues and automate remediation to improve reliability resiliency and operational performance of AI features and services.
• Create durable reusable frameworks and platform componentsleveragedacross teams aligned to modern product development methodologies.
• Influence leaders and senior stakeholders across business product and technology to drive alignment and outcomes; foster a culture of diversity opportunity inclusion and respect.
• Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed code quality and operational outcomes at scale (e.g. AI-driven PR review assistance test generation/maintenance release readiness checks incident triage and root-cause acceleration) while defining guardrails for validation security resiliency and reuse across teams.
• 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 at scale.

Required qualifications capabilities and skills

• Formal training or certification on software engineering concepts and 7 years applied experience
• Hands-on experience delivering system design application development testing and operational stability for large-scale platforms and services.
• Expertproficiencyin one or more programming languages (e.g. Python Java Scala Go) with strong code quality testing and debugging practices.
• Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams including setting standards for human-in-the-loop validation auditability/traceability of changes and secure handling of sensitive data.
• Strong understanding of responsible AI use and control expectations in engineering workflows including security/resiliency implications data sensitivity and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.
• Proven ability to design andoperateML/LLM platforms: reproducible training pipelines experiment tracking model/data versioning and continuous evaluation.
• Practical cloud-native experience (containers orchestrationIaC observability) and experience operating production systems with clear SLOs.
• Experience applying new methods to solve complex technology problems across one or more technical disciplines (platform engineering ML systems data engineering distributed systems).
• Strong communicationskills: able topresent toand influence senior leaders/executives translating complex technical topics into clear decisions and trade-offs.
• Strong understanding of business outcomes and product delivery and ability to align platform roadmaps to measurable impact.

Preferred qualifications capabilities and skills

• Practical experience with distributed compute and scalable model training/fine-tuning (e.g. Ray and/or comparable distributed frameworks) including performance cost andreliabilitytrade-offs.
• Experience building model development platforms for LLMs/agentic systems (fine-tuning evaluation harnesses retrieval/tooling integration prompt/agent testing).
• Experience with modernMLOpstoolchains (CI/CD formodels model registries feature/data stores governance workflows) andproductionML operations.
• Background in LLM evaluation benchmarking red-teaming and quality measurement (offline online) including experimentation and A/B testing.
• Experience designing multi-tenant platforms reusable frameworks and developer self-service capabilities at enterprise scale.
• Strong security-by-design experience for ML systems (secrets access control data handling supply chain controls) and resiliency engineering.

FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such an employment offer for this position is contingent on JPMorganChases review of criminal conviction history including pretrial diversions or program entries.