Staff AI Engineer

Create Music Group•Los Angeles, CA

4d•$190,000 - $210,000•Hybrid

About The Position

This Staff AI Engineer role sits at the intersection of AI systems architecture and full-stack product engineering within CMG's AI & ML Engineering org. The person in this seat is the technical anchor for CreateOS's agentic AI layer — owning the design, deployment, and ongoing reliability of the LLM-powered features and agent infrastructure that run across the platform. Day-to-day, this role spans three areas: building and scaling the agent platform (orchestration, RAG pipelines, memory, routing, guardrails); shipping production AI features end-to-end from data model to UI; and serving as the primary technical voice with product, M&A, A&R, and Marketing stakeholders. Unlike a research or prototype role, the bar here is production — systems serving real users at scale, with the observability and reliability expectations of revenue-critical software. Beyond individual contribution, this engineer helps set the architectural patterns and development standards the broader AI team builds against — mentoring the ML Engineer, shaping tooling and primitives, and representing AI Engineering in cross-functional and executive forums. It's a high-autonomy seat for someone who's comfortable deciding what to build and why, not just how. The right candidate brings 6+ years of software engineering experience, at least 3 of which are hands-on with production agentic AI or LLM systems, along with deep fluency in RAG architectures, eval frameworks, and modern AI-native development practices.

Requirements

6+ years of software engineering experience with a track record of shipping production systems
3+ years hands-on building production agentic AI or LLM-powered systems — not prototypes, not demos, systems serving real users
Demonstrated technical ownership of a non-trivial system or platform — you've been the person responsible for the architecture, not just the implementer
Deep proficiency in a backend language (Python or Node.js) and working fluency in a modern frontend framework (React, Next.js)
Strong experience with RAG systems, vector databases, and embedding-based retrieval — including design tradeoffs (chunking, hybrid search, reranking, freshness)
Experience designing eval frameworks for LLM systems — output quality, hallucination detection, regression testing, offline + online eval
Experience designing and documenting RESTful APIs
Proficiency in relational databases (PostgreSQL); comfortable writing and optimizing SQL
Solid understanding of containerization (Docker), Kubernetes, and CI/CD
Proficiency with AI-native development tools (Cursor, Claude Code)
Ability to operate with high autonomy and ambiguity — you decide what to build and why, not just how

Responsibilities

Architect AI agents and the orchestration, tool-use, memory, and routing patterns they share — building toward a cohesive agent platform for CreateOS
Design RAG pipelines, retrieval architectures, and semantic search grounded in CreateOS structured data (contracts, royalty statements, catalog metadata, deal terms)
Define guardrails, evaluation, observability, and human-in-the-loop standards so agentic systems ship safely and stay measurable
Drive model, prompt, and tool-use choices — including cost and latency tradeoffs at production scale
Integrate frontier LLMs (OpenAI, Anthropic) and selected open-source models into user-facing features across CreateOS modules
Partner with product, M&A, A&R, and Marketing stakeholders to turn ambiguous business needs into well-scoped AI features
Run discovery conversations with internal users to validate the problem before committing engineering investment
Translate non-technical requirements into clear technical specs; surface tradeoffs (cost, latency, accuracy, scope) early and explicitly
Negotiate scope, timelines, and acceptance criteria with cross-functional partners
Demo work-in-progress regularly; iterate based on real user feedback rather than assumed requirements
Document decisions and rationale so stakeholders stay aligned across long-running initiatives
Ship full-stack AI-native features end-to-end — chat interfaces, copilot tools, workflow automation surfaces — from data model to UI
Deploy and maintain production AI services with the reliability, observability, and performance expectations of a revenue-critical system
Maintain CI/CD, testing standards, and code quality for the AI application layer
Partner with Data Engineering to consume internal pipelines (dbt, Airflow), third-party feeds (DSPs, distributors), and event-driven flows into product surfaces
Drive architecture decisions for the AI application layer in collaboration with the VP of AI & ML Engineering
Help shape the patterns, primitives, and tooling the team builds against — so future hires ramp fast and ship safely
Mentor the ML Engineer and future AI hires; raise the bar on code review, design docs, and eval rigor
Represent AI engineering in cross-functional forums with product, data, and executive stakeholders
Communicate tradeoffs clearly across engineering, product, and business audiences