Software Engineer III- AI, LLM & Data Platforms

JPMorgan Chase & Co.; Plano (TX); On-site; USD 110,000 - 160,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Software Engineer III to advance secure, scalable AI-enabled platforms within Consumer & Community Banking.

You will collaborate across product, data, security, and risk teams to design and deploy trusted technology products. As a core technical contributor, you will build reusable services for LLM-powered agents, implement evaluation and monitoring, and ensure robust security and governance in production environments while maintaining high code quality and

Qualifications

• Formal training or certification on software engineering concepts.
• 3+ years applied experience in software engineering.
• Proficiency in Python with strong fundamentals including testing, VCS, and code review.
• Experience building and operating production services with reliability focus.
• Hands-on ML/LLM delivery in production environments, with monitoring.
• Experience using AI-assisted dev tools and establishing validation standards.
• Understanding of responsible AI, data sensitivity, and secure adoption practices.
• Knowledge of prompt engineering and retrieval-augmented generation (RAG).
• Ability to design maintainable systems and balance speed, risk, and sustainability.
• Strong communication and cross-team collaboration skills.

Responsibilities

• Deliver end-to-end platform features for AI-enabled use cases from design to production deployment.
• Drive team adoption of enterprise AI-assisted engineering practices to improve quality and speed.
• Apply SDLC tools and AI-assisted development to improve automation value.
• Build and maintain reusable services for LLM-powered agents at scale.
• Develop evaluation, testing, and quality measurement for LLM systems.
• Develop observability (tracing, metrics, logs) to support operations and troubleshooting.
• Apply security, governance-by-design patterns including guardrails and access controls.
• Collaborate with product managers to define success metrics and prioritize work.
• Partner with engineering, data, security, and risk stakeholders to ensure secure, scalable solutions.
• Produce clear documentation and reference implementations to accelerate adoption.

Skills

Python programming

Software engineering fundamentals

Testing practices

Code reviews

AI-assisted development

Secure development

Prompt engineering

Collaboration

Education

Software engineering certification

Tools

Docker

Kubernetes

LangGraph

LlamaIndex

CI/CD tooling

Databricks

Job description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the Consumer & Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job responsibilities:

• Deliver end-to-end platform features for agent-based artificial intelligence use cases, from technical design through production deployment, monitoring, and ongoing improvements
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• 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.
• Build and maintain reusable services and components that enable teams to create, evaluate, and operate LLM-powered agents at scale
• Implement evaluation and testing approaches for LLM systems, including quality measurement, regression testing, and error analysis to improve reliability over time
• Develop observability capabilities such as tracing, metrics, logs, and analytics to support healthy operations and fast troubleshooting
• Apply security, safety, and governance-by-design patterns, including guardrails, access controls, and audit-ready operational practices aligned to security and governance requirements
• Collaborate with product managers and stakeholders to define success metrics, prioritize work, and deliver measurable business impact
• Partner across engineering, data, security, and risk and compliance stakeholders to ensure solutions are secure, stable, and scalable
• Produce clear documentation and reference implementations that accelerate adoption and promote responsible, consistent engineering practices

Required qualifications, capabilities and skills:

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Proficiency in Python with strong software engineering fundamentals, including testing practices, version control, and code review
• Experience building and operating production services, including incident readiness, performance tuning, and operational stability for data-intensive systems
• Hands-on experience delivering machine learning and/or LLM-enabled features into production environments, including monitoring and post-deployment iteration
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Practical experience with prompt engineering and retrieval-augmented generation (RAG), including evaluation methods and quality measurement
• Ability to design maintainable systems and make sound technical decisions through ambiguity, balancing delivery speed, risk, and long-term sustainability
• Strong communication skills, including the ability to explain technical trade-offs to both technical and non-technical stakeholders
• Strong collaboration skills and demonstrated ability to partner effectively across teams to deliver outcomes

Preferred qualifications, capabilities and skills:

• Experience with agent orchestration frameworks (for example, LangGraph, LlamaIndex, or comparable orchestration approaches) and LLM evaluation tooling
• Experience with continuous integration/continuous delivery (CI/CD) practices and production deployments using Docker and Kubernetes
• Familiarity with vector databases, embedding pipelines, and related approaches used in retrieval-augmented generation solutions
• Experience with cloud and machine learning platforms such as Amazon Web Services or Databricks (or comparable platforms)
• Experience contributing to governance, validation approaches, or guardrail frameworks for enterprise-scale artificial intelligence solutions