Software Engineer III- AI, LLM & Data Platforms

JPMorgan Chase & Co. • Plano, TX

4d

About The Position

As a Software Engineer III at JPMorganChase within Consumer & Community Banking, you will contribute to an agile team building and enhancing secure, stable, and scalable technology products. The role focuses on delivering platform capabilities for agent-based artificial intelligence and large language model (LLM) use cases, from design through production deployment and ongoing operation. You will build reusable services and components, implement evaluation and observability capabilities, and apply security, safety, and governance-by-design practices. You will also promote effective, validated use of enterprise-authorized AI-assisted engineering tools and collaborate across product, engineering, data, security, risk, and compliance teams to deliver measurable business outcomes.

The position calls for production software engineering experience, proficiency in Python, and hands-on delivery of machine learning or LLM-enabled features. It includes developing testing and evaluation methods for LLM systems, supporting operational readiness and troubleshooting, and producing documentation and reference implementations that encourage consistent, responsible engineering practices. Preferred experience includes agent orchestration frameworks, CI/CD, Docker, Kubernetes, vector databases, cloud and machine learning platforms, and enterprise AI governance approaches.

Requirements

• Formal training or certification in software engineering concepts and at least 3 years of applied experience.
• Proficiency in Python and 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, including monitoring and post-deployment iteration.
• Experience leading effective use of approved AI-assisted software development tools, 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, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption.
• 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 amid ambiguity, balancing delivery speed, risk, and long-term sustainability.
• Strong communication skills, including explaining technical trade-offs to technical and non-technical stakeholders.
• Strong collaboration skills and demonstrated ability to partner effectively across teams to deliver outcomes.

Nice To Haves

• Experience with agent orchestration frameworks such as LangGraph, LlamaIndex, or comparable approaches, and LLM evaluation tooling.
• Experience with CI/CD practices and production deployments using Docker and Kubernetes.
• Familiarity with vector databases, embedding pipelines, and related approaches used in RAG 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 AI solutions.

Responsibilities

• Deliver end-to-end platform features for agent-based AI use cases, from technical design through production deployment, monitoring, and ongoing improvement.
• Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing validation standards and promoting reusable patterns.
• Use software development lifecycle tools, including authorized AI-assisted development and automation capabilities, to improve the value of 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.
• Develop observability capabilities such as tracing, metrics, logs, and analytics to support healthy operations and troubleshooting.
• Apply security, safety, and governance-by-design patterns, including guardrails, access controls, and audit-ready operational practices aligned with security and governance requirements.
• Collaborate with product managers and stakeholders to define success metrics, prioritize work, and deliver measurable business impact.
• Partner with engineering, data, security, risk, and compliance stakeholders to ensure solutions are secure, stable, and scalable.
• Produce documentation and reference implementations that accelerate adoption and promote responsible, consistent engineering practices.

Benefits

• Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed