Principal Software Engineer - AI Foundations

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. in Jersey City, NJ, is seeking a Principal Software Engineer to join the Chief Data and Analytics Office. You will provide engineering excellence on an agile team to deliver trusted technology products in a secure, scalable way.

You will design AI-enabled applications, lead ML/LLM platform efforts, and drive end-to-end MLOps, data/versioning, and production readiness across portfolios. The role emphasizes collaboration with senior stakeholders, governance of AI workflows,

Qualifications

• 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.
• Expert proficiency in 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 across teams, including setting standards for human-in-the-loop validation and secure data handling.
• Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications and risk-based governance.
• Proven ability to design and operate ML/LLM platforms: reproducible training pipelines, experiment tracking, model/data versioning.
• Practical cloud-native experience (containers, orchestration, IaC, observability) and operating production systems with clear SLOs.
• Experience applying new methods to solve complex technology problems across platform engineering, ML systems, data engineering, distributed systems.
• Strong communications skills: ability to present to and influence senior leaders/executives.

Responsibilities

• Design, build, and troubleshoot AI-enabled applications and AI services, delivering scalable solutions.
• Develop secure, high-quality production code; review, debug, and improve code.
• Own and support SDK and service integrations, ensuring reliability and performance.
• Build and ship AI-powered features, including prompt design and SDK/REST integrations.
• Design and implement end-to-end MLOps capabilities including data/model versioning and CI/CD for models.
• Contribute to next-generation training techniques and productize them into platform primitives.
• Automate remediation to improve reliability, resiliency, and operational performance.

Skills

Python/Java/Scala/Go

Agentic AI development

Cloud-native / IaC / containers

Strong communication

Platform engineering / ML systems

Tools

Kubernetes

CI/CD tooling

Ray

Job description

If you are looking for a game-changing career, working for one of the world's leading financial institutions,you’vecome 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 firm’s 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, orchestration,IaC, 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, and reliabilitytrade-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 JPMorganChase’s review of criminal conviction history, including pretrial diversions or program entries.