Principal Software Engineer - AI Foundations

Overview

If you are looking for a game-changing career, working for one of the world s leading financial institutions, you ve come to the right place.

As a Principal Software Engineer at JPMorgan

Chase within the Chief 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.

• 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 experience required).
• Design and implement end-to-end MLOps capabilities including data/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.
• Identify recurring issues and automate remediation to improve reliability, resiliency, and operational performance of AI features and services.
• Create durable, reusable frameworks and platform components leveraged across 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.
• Architect and govern agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale, while defining guardrails for validation, security, resiliency, and reuse across teams.
• Apply 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.

• 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, 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 and operate ML/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 communication skills: able to present to and 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.

• Practical experience with distributed compute and scalable model training/fine-tuning (e.g., Ray and/or comparable distributed frameworks), including performance, cost, and reliability trade-offs.
• Experience building model development platforms for LLMs/agentic systems (fine-tuning, evaluation harnesses, retrieval/tooling integration, prompt/agent testing).
• Experience with modern MLOps tool chains (CI/CD for models, model registries, feature/data stores, governance workflows) and production ML 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…