Senior Applied AI Engineer – Enterprise Systems

Role: Senior Applied AI Engineer Enterprise Systems
Location: Remote (US) or Los Angeles (preferred)
Compensation:
Remote: $70000$120000
Los Angeles: $110000$160000
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.

Were looking for an engineer who has evolved from systems engineering into applied AIsomeone 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 youll 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.

Youll 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 dont just workthey continue working reliably after deployment.

Youll 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.

Were looking for systems engineers who naturally evolved into building AI-powered softwarenot AI hobbyists who recently discovered infrastructure.

You likely have:

• 36 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 deploymentnot 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.
• Youre looking for a role focused on developing foundation models.
• You prefer infrastructure-only work without building production software.

Youll 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 youre excited about applying AI to solve real enterprise problemsand owning those systems long after deploymentwed love to hear from you