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# Senior/Staff Software Engineer, Agent

- **Company:** VIBE HIGHER SOCIETY INC.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Cursor, Memory Management, Performance Tuning, Reliability Engineering, Next.js, Datadog, ReactJS, Large Language Models, Claude Code, Multi-Agent Systems, Agentic-AI, Kubernetes, Front End Software Development, Claude, Evaluation of Large Language Models, Agent SDK, Automation Anywhere
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25196_d39d9d5ba723cc3bcdfcfbe7c6136bec

## About the Role

* 7+ years of full-stack engineering experience shipping and owning production systems at scale.
 * 2+ years building LLM-powered agents in production (not just prototypes/demos).
 * Deep, hands-on experience with agent architecture: tool use, context/memory management, multi-step planning, sub-agent orchestration, and long-running workflows.
 * Experience designing evaluation frameworks and observability for agentic systems.
 * Strong system design and data modeling skills; comfortable reasoning about state, schemas, and APIs.
 * Experience authoring reusable Agent Skills or equivalent abstractions.
 * Demonstrated technical leadership and ability to set architectural direction.
 * A strong AI-assisted coding practice (experience with Claude Code, Codex, Cursor, or similar).
 * High product sense and strong communication skills., * Practical experience building MCP servers and CLI tooling for agents.
 * Experience with agent orchestration frameworks (Claude Agent SDK, LangGraph) and LLM evaluation/observability tooling.
 * Hands-on experience with image/video generation models and creative asset pipelines.
 * Experience with guardrails, safety, and cost controls for autonomous systems.
 * Startup experience or ownership of a product surface from 0 to 1., Full-stack development System Design Data Modeling Agent architecture Observability Evaluation framework design Technical leadership Product thinking Communication Mentorship Reliability engineering Performance optimization CLI tooling Frontend development

## Description

Lead the design and implementation of OpenArt's agent architecture, owning end-to-end agent infrastructure (harness, orchestration, memory, evals, tooling) to power large-scale creative AI workflows and ship production frontend experiences., We are hiring a Senior/Staff Software Engineer to lead the agentic architecture for OpenArt's next generation of creative products. You will own the full agent harness-tooling, orchestration, memory, evaluation, and reliability-and ship production frontend experiences that enable large-scale, long-running creative workflows., * Own the end-to-end architecture of the agent harness powering conversational and autonomous creative workflows: tool use, context/memory management, multi-step planning, and sub-agent orchestration.

 * Design long-running planning and execution for creative tasks (scene planning, storyboarding, asset generation, revision loops) across multiple model vendors, with clear failure and recovery modes.
 * Build and maintain MCP servers and CLI tooling that connect agents to internal services, external model vendors, and creative asset pipelines.
 * Author reusable Agent Skills and structured workflows; define patterns for other engineers to extend the system.
 * Build evaluation and observability stacks: automated evals, regression suites, tracing, and quality gates.
 * Drive reliability, latency, and cost-per-run as core metrics.
 * Ship polished production frontend experiences using React/Next.js for agent-driven products.
 * Set engineering standards: architecture reviews, mentorship, and technical direction.
 * Track the agent ecosystem (MCP, Agent Skills, orchestration frameworks, model capabilities) and decide what to adopt.
 * Partner with founders, product, design, and GTM to translate user needs into technical direction and communicate trade-offs., * A clear, documented agent harness architecture adopted by the team.
 * A production agent workflow you designed is running for real users.
 * Eval and observability loops catch regressions before users see them.
 * Measurable improvements in reliability, latency, or cost-per-run on a key workflow.
 * Established feedback loop with design, founders, and users; setting direction for agent engineering.