Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
Mountain View, CA, United States
Posted
21 hours ago
PythonTypeScript
Job Description
Agentic Feature Development & Full Stack Delivery
• Design, build, and ship agentic features directly within EAS — autonomous workflow agents, multi-step task orchestration, tool-calling loops, and human-in-the-loop interaction patterns
• Own agentic features end to end — from architecture and implementation through testing, hardening, and production deployment
• Identify high-value automation opportunities within EAS workflows and translate them into well-scoped, shippable features
• Integrate new agentic capabilities cleanly into an existing product codebase without disrupting existing functionality
• Own the full stack of agentic feature delivery — backend orchestration, API integration, and front-end surfaces that expose agent capabilities to enterprise users
• Build RAG pipelines over structured and unstructured data to power intelligent retrieval, decision support, and workflow automation within EAS
• Build memory and state management systems that allow agents to maintain context across multi-step, long-running workflows
Agentic AI — Core Requirement
• Build production-grade agentic systems with the reliability, observability, and failure handling that enterprise software demands
• Design evaluation harnesses to continuously test agent accuracy, behavioural consistency, and edge case handling
• Build guardrails, fallback logic, and escalation patterns that ensure agents degrade gracefully and keep users in control
• Instrument agentic features with logging, tracing, and monitoring to observe agent behaviour in production and iterate with confidence
• Participate in architecture and design reviews — contributing agentic expertise and maintaining quality standards across short delivery cycles
• Contribute to shared agentic patterns and reusable components that raise the capability baseline for the broader EAS engineering team
• Define and implement evaluation frameworks to measure agent accuracy, task completion, and behavioural consistency across diverse inputs and edge cases
• Experience with both automated eval pipelines (unit-level tool call testing, end-to-end trace evaluation) and human-in-the-loop review workflows for validating agent outputs in production
Required Experience
• Demonstrated hands-on experience building agentic AI capabilities inside a product — multi-step orchestration, tool-calling agents, memory systems, and human-in-the-loop flows used by real users in production
• Deep familiarity with agent frameworks — LangGraph, Anthropic SDK, OpenAI Agents SDK, CrewAI, AutoGen, or similar — applied in product feature delivery, not research
• Strong understanding of agentic design patterns: planning loops, tool registries, context window management, agent state machines, and failure handling
• Experience building and integrating RAG pipelines into product workflows
• Experience building production guardrails and evaluation frameworks for agentic features
• Strong full-stack engineering skills with production experience in Python and/or TypeScript
• 5+ years of full-stack software engineering with a strong shipping record
• 1+ years of hands-on experience building agentic AI features in production products
Preferred
• Experience integrating agentic capabilities into SaaS or fintech products at scale
• Familiarity with Intuit''s developer platform or QuickBooks APIs
• Exposure to regulated or high-accuracy domains where agent reliability and auditability are non-negotiable
• Design, build, and ship agentic features directly within EAS — autonomous workflow agents, multi-step task orchestration, tool-calling loops, and human-in-the-loop interaction patterns
• Own agentic features end to end — from architecture and implementation through testing, hardening, and production deployment
• Identify high-value automation opportunities within EAS workflows and translate them into well-scoped, shippable features
• Integrate new agentic capabilities cleanly into an existing product codebase without disrupting existing functionality
• Own the full stack of agentic feature delivery — backend orchestration, API integration, and front-end surfaces that expose agent capabilities to enterprise users
• Build RAG pipelines over structured and unstructured data to power intelligent retrieval, decision support, and workflow automation within EAS
• Build memory and state management systems that allow agents to maintain context across multi-step, long-running workflows
Agentic AI — Core Requirement
• Build production-grade agentic systems with the reliability, observability, and failure handling that enterprise software demands
• Design evaluation harnesses to continuously test agent accuracy, behavioural consistency, and edge case handling
• Build guardrails, fallback logic, and escalation patterns that ensure agents degrade gracefully and keep users in control
• Instrument agentic features with logging, tracing, and monitoring to observe agent behaviour in production and iterate with confidence
• Participate in architecture and design reviews — contributing agentic expertise and maintaining quality standards across short delivery cycles
• Contribute to shared agentic patterns and reusable components that raise the capability baseline for the broader EAS engineering team
• Define and implement evaluation frameworks to measure agent accuracy, task completion, and behavioural consistency across diverse inputs and edge cases
• Experience with both automated eval pipelines (unit-level tool call testing, end-to-end trace evaluation) and human-in-the-loop review workflows for validating agent outputs in production
Required Experience
• Demonstrated hands-on experience building agentic AI capabilities inside a product — multi-step orchestration, tool-calling agents, memory systems, and human-in-the-loop flows used by real users in production
• Deep familiarity with agent frameworks — LangGraph, Anthropic SDK, OpenAI Agents SDK, CrewAI, AutoGen, or similar — applied in product feature delivery, not research
• Strong understanding of agentic design patterns: planning loops, tool registries, context window management, agent state machines, and failure handling
• Experience building and integrating RAG pipelines into product workflows
• Experience building production guardrails and evaluation frameworks for agentic features
• Strong full-stack engineering skills with production experience in Python and/or TypeScript
• 5+ years of full-stack software engineering with a strong shipping record
• 1+ years of hands-on experience building agentic AI features in production products
Preferred
• Experience integrating agentic capabilities into SaaS or fintech products at scale
• Familiarity with Intuit''s developer platform or QuickBooks APIs
• Exposure to regulated or high-accuracy domains where agent reliability and auditability are non-negotiable
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