Senior AI Workflow & Systems Engineer

TubeScience•Los Angeles, CA

23h•$70,000 - $160,000•Remote

About The Position

TubeScience is a data-driven creative studio producing performance advertising at massive scale — and we're growing fast. We're looking for a Senior AI Workflow & Systems Engineer to be the most technically sophisticated AI builder in the company. You'll sit in IT but serve everyone — owning the infrastructure, deployments, and systems that make our AI initiatives real, and unblocking every team that's building on top of them. This is a systems and deployment role for someone genuinely excited about where AI is taking enterprise engineering. You won't just design workflows — you'll own the infrastructure they run on, keep them running reliably, and be the expert other teams call when things break or they hit a wall. You are the architect, the deployer, the maintainer, and the unlocker — all in one. When there's no PM driving an AI initiative, you'll step in and own it end-to-end.

Requirements

4–6+ years in software engineering, DevOps, or systems engineering — with hands-on AI/ML experience
Strong foundation as a software, systems, or DevOps engineer who has grown into AI — not the other way around
Proven experience deploying and managing production applications on Vercel, AWS, GCP, or equivalent
Hands-on with LLMs, generative AI, and orchestration tools (n8n, Make, Zapier, LangChain, or equivalent)
Proven REST API integration experience with solid edge-case handling
Experience building or maintaining cloud-based agents and serverless infrastructure
Strong Python and/or JavaScript/Node.js — clean, production-grade code
Solid understanding of deployment pipelines, CI/CD, environment management, and secrets handling
Experience with vector databases and embedding-based retrieval
Comfortable with cloud infrastructure (AWS and/or GCP) and cloud-native application patterns
Familiarity with monitoring, logging, and alerting for production systems
Highly autonomous — identifies problems and ships solutions without waiting to be asked
Effective communicator across technical and non-technical audiences
Strong product instincts: can step into ownership of an initiative when there's no PM in the room
Calm under pressure; reliable when other teams are blocked and need answers fast
Comfortable working across many different teams and problem domains simultaneously

Nice To Haves

Experience with AI agent frameworks
Background in high-volume performance advertising, media, or creative production
Experience with AI in a production context
Multi-step agentic pipeline design or large-scale workflow orchestration
Experience with data pipelines or BI tooling

Responsibilities

Build and deploy LLM-powered applications and agent-based workflows that eliminate manual effort across the company
Design multi-step agentic pipelines — tool use, RAG, structured outputs — built for production, not demos
Integrate AI workflows with TubeScience's existing systems via REST APIs, webhooks, and custom integrations
Develop automation pipelines
Evaluate emerging AI tooling and own build-vs-buy decisions
Own deployment and management of AI workflows and applications on Vercel and cloud platforms
Build and maintain the infrastructure that supports TubeScience's AI initiatives — including cloud-based agents, serverless functions, and supporting services
Design for resilience: logging, error handling, alerting, and monitoring across all deployed systems
Manage secrets, environment configs, and deployment pipelines across environments
Align with engineering on architecture, scalability, and infrastructure decisions
Serve as the go-to technical resource for teams across TubeScience building AI-powered workflows and apps
Deploy, maintain, and improve departmental AI tools — owning the full lifecycle from build to production
Debug and unstick builders across the company when they hit technical walls
Translate team-specific business needs into precise technical requirements and actionable solutions
Serve as final escalation for complex AI and systems issues teams can't resolve on their own
Proactively audit AI systems and workflows for reliability issues, inefficiencies, and improvement opportunities
When there's no dedicated PM on an AI initiative, step in: define the problem, scope the solution, and drive it to completion
Prototype emerging AI tools and frameworks and bring the best ones into TubeScience's stack
Document every system thoroughly so the company can run it confidently