Senior Applied AI Engineer – Enterprise Systems

TubeScience; Los Angeles (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

TubeScience is looking for a Senior AI Workflow & Systems Engineer in Los Angeles, California. This role involves building and maintaining the AI infrastructure for various teams, deploying AI workflows, and serving as a technical resource.

The ideal candidate will have extensive experience in software engineering, particularly in AI/ML, along with skills in deployment on cloud platforms like Vercel and AWS. Leadership and communication skills are essential as you will empower various teams across the organization.

Qualifications

• 4–6+ years of experience in software engineering or DevOps with a focus on AI.
• Proven experience deploying applications on cloud platforms.
• Comfortable with managing production-ready AI systems.

Responsibilities

• Build and maintain AI-enabled applications and workflows.
• Own deployment and management of AI workflows on cloud services.
• Serve as a technical resource for cross-functional teams.

Skills

4–6+ years in software engineering, DevOps, or systems engineering with AI/ML experience

Experience with deploying production applications on Vercel, AWS, GCP

Hands-on with LLMs and generative AI

Proven REST API integration experience

Highly autonomous problem-solving

Tools

Vercel

AWS

GCP

n8n

Zapier

Job description

Senior AI Workflow & Systems Engineer

Build and run the AI infrastructure that powers every team at TubeScience.

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.

The Role

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.

What You'll Own

• AI Workflow Engineering
  • 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
• Infrastructure & Deployment
  • 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
• Cross-Functional Enablement
  • 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
• Ownership & Improvement
  • 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

What We're Looking For

• Background & Experience
  • 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
• Soft Skills
  • 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
• Bonus Points
  • 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