Senior AI Engineer (Workflows & Systems)

Role overview An internal, forward-deployed engineering position focused on applying modern AI to real operational bottlenecks inside a high-volume creative production business. The role combines systems engineering with applied AI: designing, building, deploying, and owning production software that automates enterprise workflows and keeps working reliably after launch. The team uses the latest frontier and open-weight models end-to-end, from prototyping through deployment and ongoing operations.

Responsibilities - 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 with reliable orchestration - Deploy AI systems with observability, monitoring, rollback strategies, and operational safeguards - Investigate production issues, analyze logs, debug failures, and lead incident response when systems break - Partner with product, operations, creative, engineering, and business teams to find high-impact automation opportunities - Prototype, validate, ship, and iterate quickly based on real production performance and business outcomes

Requirements - 3+ years of professional software or systems engineering experience building production software used by real users or internal teams - Background that moved from systems engineering into applied AI (platform, backend, enterprise systems, internal developer platforms, or infrastructure with significant software development) - Strong Python engineering skills - Hands-on experience integrating modern LLMs into production systems using APIs such as OpenAI or Anthropic, plus frameworks like LangGraph, MCP, or similar - Experience designing systems that coordinate multiple APIs, databases, services, and enterprise applications - Solid understanding of distributed systems and production operations: debugging, logging, monitoring, and post-launch improvement

Nice to have - Building production systems at a large technology company - Multi-agent systems or orchestration frameworks such as LangGraph, MCP, or Temporal - Event-driven architectures, Docker and Kubernetes, and AWS, GCP, or Azure - CI/CD pipelines and observability platforms such as Datadog, Grafana, or OpenTelemetry - Internal developer platforms or enterprise integrations

Benefits and work setup - On-site role in Los Angeles ($110,000–$160,000 base) or US-based remote ($70,000–$120,000 base) - Hiring process described as roughly three conversations and a short practical assignment, about 17 business days end-to-end