Applied AI Engineer

MethodHub; Mountain View (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

MethodHub is seeking a Senior Applied AI Engineer – Agentic Systems in Mountain View, CA for long-term engagement. You will design, build, and ship autonomous, agentic features directly within EAS, including multi-step orchestration, tool-calling loops, and human-in-the-loop patterns.

You will own end-to-end delivery—architecture, implementation, testing, deployment, and maintenance—while enabling enterprise-grade reliability, observability, and scalability.

Qualifications

• Hands-on experience delivering agentic AI features in production
• Strong full-stack experience with Python/TypeScript
• Experience with RAG pipelines and memory systems
• Familiarity with agent frameworks (LangGraph, Anthropic/OPenAI SDKs)
• Ability to design evaluation harnesses and guardrails

Responsibilities

• Design, build, and ship agentic features within the product
• Own architecture, implementation, testing, and production deployment
• Create memory/state management to maintain context over long workflows
• Develop tool-calling loops, multi-step orchestration, and human-in-the-loop patterns
• Integrate agentic capabilities into existing product codebase with minimal disruption
• Build and evaluate RAG pipelines for structured/unstructured data
• Implement logging, tracing, and monitoring for production reliability

Skills

Full-stack engineering

Python

TypeScript

Agent frameworks

RAG pipelines

Memory systems

Observability

Unit testing

Tools

LangGraph

Anthropic SDK

OpenAI Agents SDK

CrewAI

AutoGen

Job description

Job Title: Senior Applied AI Engineer – Agentic Systems

Location: Mountain View, CA

Duration: Long Term

Client: Direct

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, behavioral 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 behavior 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 behavioral 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