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, 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, 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.