oracle
Software Engineer III- AI, LLM & Data Platforms
JPMorgan Chase
Plano, USA
Full-time
Mid · 3+ years experience
Onsite
Discovered 6 days ago
databricksdockerkubernetespython
Key Skills for This Role
databricksdockerkubernetespython
Full Job Posting
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
- 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
- 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
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
- 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)
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
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