Staff Software Engineer - AI Foundation

AI Foundation platform

Platform engineering

• Build Python backend services and pipelines for agent evaluation, observability, and quality measurement at Intuit scale.
• Own evaluation platform components such as golden datasets, LLM-as-judge pipelines, trajectory and correctness scoring, and regression detection.
• Build agent observability infrastructure with Langfuse or equivalent OpenTelemetry-based tracing to expose agent behavior, latency, and failure modes.
• Author and maintain Kubeflow Pipelines, including DAGs, artifact tracking, caching/memoization, and deployment on internal Kubernetes infrastructure.
• Develop scalable, high-performance distributed applications and services; the work is approximately 80–95% hands-on coding.

Ownership and collaboration

• Own initiatives end to end from design through production launch, partnering with product agent teams on evaluation and observability needs.
• Experiment with customers, work through ambiguous problems, and iterate quickly.
• Use AI/LLM tooling to accelerate engineering and help shape AI-native development practices for the platform team.
• Mentor engineers on software practices, platform architecture, and AI/agent evaluation concepts.
• Partner with data scientists, product managers, and engineering teams to define agent quality and how it is measured and guaranteed.

Qualifications

• Requires a bachelor’s degree in engineering or computer science, or equivalent practical experience.
• Requires 10+ years building and operating production distributed backend systems.
• Requires strong Python proficiency and software engineering fundamentals including APIs, data modeling, testing, and CI/CD.
• Requires hands-on LLM or agent systems experience, such as prompt engineering, embeddings, RAG, LLM-as-judge evaluation, or agent frameworks.
• Requires ML pipeline orchestration experience such as Kubeflow Pipelines, Argo Workflows, or similar, including authoring, artifact/versioning, and Kubernetes deployment.
• Requires familiarity with observability or tracing for LLM or distributed applications, such as Langfuse, OpenTelemetry, or comparable tools.
• Requires experience with at least one major cloud provider: AWS, GCP, or Azure.
• Seeks an independent self-starter with strong problem-solving, delivery, communication, and cross-functional partnership skills.

Compensation

• The posting describes a competitive compensation package that may include cash bonus, equity rewards, and benefits, subject to applicable plans and programs.
• Pay is based on job-related knowledge, skills, experience, and work location; no pay amount or cadence is stated.