Staff AI Engineer

Create Music Group; Los Angeles (CA); On-site; USD 190,000 - 210,000; Full time

Job summary

Create Music Group in Los Angeles seeks a Staff AI Engineer to anchor the AI layer across CreateOS, building scalable agent platforms and production-ready features. You will own design, deployment, and reliability of LLM-powered capabilities and governance, collaborating with product, M&A, A&R, and Marketing.

You will drive architecture, mentor engineers, and set patterns for the team while delivering end-to-end AI-enabled products that scale for real users with strong observability.

Qualifications

• 6+ years engineering experience shipping production systems.
• 3+ years hands-on building production agentic AI/LLM systems.
• Ownership of a non-trivial system or platform.
• Fluency in Python or Node.js and frontend with React/Next.js.
• Strong experience with RAG, vector stores and embedding retrieval.
• Experience designing RESTful APIs and SQL (PostgreSQL).
• Docker, Kubernetes and CI/CD proficiency.
• AI-native development tools experience.

Responsibilities

• Architect AI agents and orchestration patterns for a cohesive CreateOS platform.
• Design and optimize RAG pipelines and semantic search using structured data.
• Define guardrails, observability, and safety for production agentic systems.
• Lead cost and latency tradeoffs for production AI features and tooling.
• Mentor ML engineers and shape tooling, patterns, and development standards.
• Represent AI Engineering in cross-functional and executive forums.

Skills

Production systems

Agentic AI

Python/Node.js

React/Next.js

RAG systems

RESTful APIs

PostgreSQL

Docker/Kubernetes

Cursor/Claude Code tools

Autonomy

Tools

Cursor

Claude Code

Job description

Established in 2015, Create Music Group is a leading music and entertainment company. The company operates as a record label, distribution company, and entertainment network which generates over 15 billion music streams each month on DSP’s. Named #2 on the Inc 5000 Fastest Growth Companies in America in 2020, the company has grown exponentially by leveraging its owned IP with its media and technology platform. The company works with superstar artists, major and independent record labels, and global media brands. It operates a number of companies including Label Engine, one of the largest independent music distribution platforms in the world, with over 75,000 artists and 5,000 label clients; and Flighthouse, a digital entertainment brand focused on Gen Z, which has more than 300 million followers across social media. Create Music Group is based in Hollywood, CA and has 400 employees worldwide.

Job Summary

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.

Responsibilities

Agent Platform & LLM Systems

• 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

Stakeholder Partnership & Requirements

• 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

Production Engineering

• 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

Technical Direction & Influence

• 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

Qualifications

• 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 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

Pay Scale

• $190,000 - 210,000 USD / year
• The final compensation within this range will be determined based on the candidate’s experience, skills, and overall fit for the role.

Details

• Must be able to attend in person in the Hollywood office 2-3 times per week
• Interviews will be a combination of virtual and in-person at our Hollywood Office.

In accordance with the Los Angeles County Fair Chance Ordinance, we will consider employment for qualified applicants with criminal histories. We evaluate candidates based on their qualifications and the nature of the offense in relation to the job for which they are applying.