Senior Staff Engineer — Intuit Agent Ready Platform, PDX

Intuit

Bengaluru

On-site

INR 6,000,000 - 9,000,000

Full time

14 days+
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Job summary

Intuit in Bengaluru invites a Senior Staff Engineer to lead architecture for the Eval platform's agent orchestration and LLM tooling. You will own virtualization layers, plan-then-execute engines, and cost-aware model routing across steps.

Join a research-driven, cross-functional environment, shaping standards, governance, and long-term roadmap while mentoring engineers and advancing scalable, auditable systems.

Qualifications

  • 12+ years of software engineering experience in large-scale distributed systems or developer platforms.
  • Hands-on experience building or operating LLM-agent systems (orchestration loops, tool use, plan-and-execute patterns).
  • Strong systems programming background (Python, Java) with virtualization, sandboxing, or IaC experience.
  • Proficiency in Python, Java, Spring Boot, GraphQL, and React.
  • Applied AI and prompt design skills for reliable, cost-aware LLM behavior.
  • Proven Staff+ capability setting cross-team technical direction.
  • Comfort navigating ambiguity in a research-driven environment.

Responsibilities

  • Core Platform Architecture: Owning and evolving the virtualization/harness layer, the agent plan-then-execute engine, and the friction detection and auto-ticketing pipeline.
  • Model Orchestration & Cost Efficiency: Transitioning from single static models to dynamic, step-level orchestration that routes tasks to cost-effective models (including open-source options) while monitoring cost and performance per step.
  • Telemetry & Auditability: Designing scalable logging, cost, and telemetry systems to ensure full auditability and cost accountability for agent runs.
  • Use-Case Normalization: Setting technical standards to normalize agent-readable use cases across differently-documented capability platforms.
  • Cross-Team & Strategic Collaboration: Partnering with capability, operations, and adjacent platform teams on friction reporting, PRD-to-action pipelines, RAG-backed architecture context, and OPA policy validation.
  • Roadmap Leadership & Governance: Guiding the roadmap from foundational tooling to automation and governance visibility.
  • Mentorship & Technical Standards: Mentoring engineers and elevating engineering standards through design reviews, architecture documentation, and hands-on contributions.

Skills

Distributed systems
LLM agents
Python
Java
Spring Boot
GraphQL
React
Prompt design
Cross-team leadership
Ambiguity tolerance

Tools

IaC

Job description

Overview

ARP assesses if Intuit internal platforms are "agentic-ready" for end-to-end AI agent development. Its Eval engine normalizes top use cases, spawns agents in virtualized environments, executes plans, auto-files issue for friction (e.g., missing docs/APIs), and re-runs checks on CI/CD deploys to prevent regressions. This role belongs to the Core Platform Framework team, which builds the Eval platform and requires research-minded engineers focused on agent orchestration and LLM tooling, distinct from the Operations/Adoption team.

Responsibilities

The selected responsibilities for the Senior Staff Engineer role encompass leading and driving key architecture initiatives for the Eval platform:

  • Core Platform Architecture: Owning and evolving the virtualization/harness layer, the agent plan-then-execute engine, and the friction detection and auto-ticketing pipeline.
  • Model Orchestration & Cost Efficiency: Transitioning from single static models to dynamic, step-level orchestration that routes tasks to cost-effective models (including open-source options and Claude) while monitoring cost and performance per step.
  • Telemetry & Auditability: Designing scalable logging, cost, and telemetry systems to ensure full auditability and cost accountability for agent runs.
  • Use-Case Normalization: Setting technical standards to normalize agent-readable use cases across differently-documented capability platforms.
  • Cross-Team & Strategic Collaboration: Partnering with capability, operations, and adjacent platform teams on friction reporting, PRD-to-action pipelines, RAG-backed architecture context, and OPA policy validation.
  • Roadmap Leadership & Governance: Guiding the roadmap from foundational tooling (plugins, RBAC, alerting) to automation (auto-fix agents, continuous re-runs) and governance visibility.
  • Mentorship & Technical Standards: Mentoring engineers and elevating engineering standards through design reviews, architecture documentation, and hands-on contributions.
Qualifications

Required:

  • 12+ years of software engineering experience, focused on large-scale distributed systems or developer platforms.
  • Hands-on experience building or operating LLM-agent systems (orchestration loops, tool use, plan-and-execute patterns).
  • Strong systems programming background (Python, Java) with virtualization, sandboxing, or IaC experience.
  • Proficiency in Python, Java, Spring Boot, GraphQL, and React.
  • Applied AI and prompt design skills for reliable, cost-aware LLM behavior.
  • Proven Staff+ capability setting cross-team technical direction.
  • Comfort navigating ambiguity in a research-driven, next-gen engineering environment.
Preferred
  • Building or contributing to LLM or agent evaluation/benchmarking frameworks.
  • Policy-as-code , RAG pipelines, or vector databases for enterprise agent grounding.
  • Cost/token optimization and multi-model routing.
  • Intuit experience or internal developer tools (CI/CD, Jira automation, developer portals).

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

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