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Role: Senior Applied AI Engineer – Enterprise Systems
Location: Remote (US) or Los Angeles (preferred)
Compensation:
• Remote: $70,000–$120,000
• Los Angeles: $110,000–$160,000
Reports to: VP of Information Systems (Eilrama)
Team: Information Systems

At TubeScience, we build software systems that combine AI, engineering, and automation to solve complex operational problems at scale.

We're looking for an engineer who has evolved from systems engineering into applied AI—someone who enjoys designing reliable production systems, integrating modern AI capabilities, and owning them in production.

This is an internal Forward Deployed Engineering role.

Rather than building products for external customers, you'll work directly with internal stakeholders to identify operational bottlenecks, architect AI-powered solutions, deploy them rapidly, and continuously improve them based on real business needs.

This is not an AI research or model-training position. We apply state-of-the-art AI models to solve enterprise problems through software engineering.

You'll own the design, implementation, deployment, and operation of AI-powered enterprise systems that automate business processes across the company.

Success in this role means building systems that don't just work—they continue working reliably after deployment.

You'll be responsible for the complete lifecycle of production AI systems, including architecture, deployment, monitoring, debugging, incident response, and continuous improvement.

• Design and build production AI applications that automate complex enterprise workflows.
• Architect agent-based systems that coordinate LLMs, APIs, internal services, databases, and business logic.
• Build reliable orchestration layers that integrate multiple tools and enterprise platforms.
• Deploy production-ready AI systems with observability, monitoring, rollback strategies, and operational safeguards.
• Investigate production issues, analyze logs, debug failures, and restore system reliability when incidents occur.
• Design scalable architectures that prioritize maintainability, resiliency, and operational excellence.
• Partner closely with Product, Operations, Creative, Engineering, and Business teams to identify high-impact automation opportunities.
• Rapidly prototype, validate, deploy, and iterate solutions based on production performance and business outcomes.
• Continuously improve existing AI systems for reliability, speed, and business impact.

We're looking for systems engineers who naturally evolved into building AI-powered software—not AI hobbyists who recently discovered infrastructure.

You likely have:

• 3–6+ years of professional software or systems engineering experience.
• Experience building and operating production software used by real users or internal business teams.
• Strong Python engineering experience.
• Experience integrating modern LLMs into production systems using frameworks such as OpenAI, Anthropic, LangGraph, MCP, or similar.
• Experience designing systems that coordinate multiple APIs, databases, services, and enterprise applications.
• Strong understanding of distributed systems, debugging, logging, monitoring, and production operations.
• Experience deploying, operating, troubleshooting, and improving production systems after launch.
• Strong architectural thinking with the ability to design complete end-to-end solutions.
• Comfort working independently in a fast-paced startup environment.

The strongest candidates typically come from backgrounds such as:

• Systems Engineering
• Platform Engineering
• Backend Software Engineering
• DevOps / Infrastructure Engineering with significant software development experience
• Internal Developer Platforms
• Enterprise Systems Engineering

They later expanded into Applied AI rather than beginning their careers in AI.

Experience at a large technology company building production systems is highly valued.

Experience with any of the following is a plus:

• Multi-agent systems
• LangGraph, MCP, Temporal, or similar orchestration frameworks
• Event-driven architectures
• Docker and Kubernetes
• AWS, GCP, or Azure
• CI/CD pipelines
• Observability platforms (Datadog, Grafana, OpenTelemetry, etc.)
• Internal developer platforms
• Enterprise integrations

• Think in systems instead of individual features.
• Enjoy solving operational problems through software engineering.
• Like building AI systems that become part of day-to-day business operations.
• Care about reliability as much as shipping speed.
• Are comfortable owning systems after deployment—not just writing the first version.
• Enjoy debugging production incidents and improving system resilience.
• Like working directly with internal stakeholders to solve real operational challenges.

• Your experience is primarily low-code workflow automation (Zapier, Make, n8n, etc.).
• Your background is mainly AI research or model training.
• Most of your AI experience comes from prototypes, hackathons, or prompt engineering.
• You prefer building proof-of-concepts over operating production systems.
• You're looking for a role focused on developing foundation models.
• You prefer infrastructure-only work without building production software.

You'll work on high-impact internal systems where your software is deployed quickly, used daily across the business, and has measurable operational impact.

We value engineers who take ownership from architecture through production, iterate rapidly, and continuously improve the systems they build.

If you're excited about applying AI to solve real enterprise problems—and owning those systems long after deployment—we'd love to hear from you