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
We are building CreateOS — a next-generation operating system for modern record labels — and AI is at the center of it. As a Full Stack AI Engineer, you will own the end-to-end design and delivery of AI-powered features that make CreateOS intelligent. This means building the agentic workflows, APIs, and interfaces through which users interact with AI copilots, predictive tools, and automated pipelines — from first concept to production deployment.
This is a high-ownership role for someone who thinks in full systems. You won't hand work off at the API boundary — you'll own the experience from the data layer to the UI. You will work directly with the VP of AI & ML Engineering and sit at the intersection of product, engineering, and applied AI. Your immediate impact will be on three of CMG's highest-priority AI initiatives: M&A catalog valuation tooling, AI-driven A&R discovery surfaces, and marketing automation agents — each directly tied to revenue growth and competitive differentiation.
You'll work in close collaboration with the ML Engineer, who owns the intelligence layer (models, features, evaluation). You own the application and orchestration layer that brings that intelligence to users.
Responsibilities
Agentic AI & LLM Systems (Primary Ownership)
- Design, build, and maintain modular AI agents that automate multi-step workflows across CreateOS (contracts, accounting, distribution, metadata)
- Own RAG pipelines, retrieval architectures, and semantic search systems grounded in CreateOS's structured business data (contracts, royalty statements, catalog metadata, etc.)
- Implement guardrails, evaluation frameworks, and human-in-the-loop controls for agentic systems
- Integrate LLMs (OpenAI, Anthropic, or open-source models) into user-facing features across CreateOS modules
Full Stack Development
- Design, build, and maintain scalable, production-grade applications across the frontend and backend
- Build intuitive, AI-native user experiences including chat interfaces, copilot-style tools, and workflow automation surfaces within CreateOS
- Own features end-to-end — from data modeling and API design to UI implementation and deployment
Platform & Infrastructure
- Deploy and maintain services using containerization and cloud platforms
- Ensure AI-powered features are reliable, observable, and performant in production
- Collaborate with the ML Engineer to integrate model outputs and feature pipelines cleanly into product surfaces
- Maintain high code quality standards through unit and integration testing, code reviews, and CI/CD pipeline ownership
- Partner with Data Engineering (who owns pipeline infrastructure) to consume and integrate internal data pipelines (dbt, Airflow), third-party API feeds (DSPs, distributors), webhook and event-driven data flows, and ETL outputs into CreateOS product surfaces
Iteration & Product Thinking
- Rapidly prototype and evaluate new AI-powered features based on internal user feedback
- Contribute to technical architecture decisions with a bias toward shipping and learning
- Communicate tradeoffs clearly across engineering, product, and business stakeholders
- Other duties as assigned
Qualifications
- 5+ years of software engineering experience with a track record of shipping production applications
- Hands-on experience building and owning agentic or multi-step AI workflows in production
- Strong proficiency in a modern frontend framework (React, Next.js) and a backend language (Python or Node.js)
- Hands-on experience integrating LLMs or AI APIs into user-facing products
- Familiarity with RAG systems, vector databases, and embedding-based retrieval
- Experience designing and documenting RESTful APIs
- Proficiency in relational databases (PostgreSQL or similar); comfortable writing and optimizing SQL queries
- Solid understanding of Kubernetes, containerization (Docker), and DevOps practices — including CI/CD pipelines, observability, and deployment workflows
- Experience with AI evaluation practices — LLM output quality assessment, hallucination detection, and building eval frameworks for agentic systems
- Proficiency with AI-native development tools (Cursor, Claude Code, or similar)
- Ability to work independently and own features from concept to deployment
Preferred Qualifications
- Previous experience at a startup or as an early/founding engineer
- Portfolio of personal or professional AI projects — RAG systems, LLM agents, copilot-style tools (GitHub links welcome)
- Familiarity with the music industry, rights management, or royalties workflows
- Experience with AI-native development tools (Cursor, Claude Code, or similar)
- Knowledge of data privacy, compliance, and responsible AI deployment considerations
Tech Stack
- Frontend: React, Next.js, TypeScript, Tailwind CSS
- Backend: Python (FastAPI), Node.js
- AI & Agent Frameworks: LangChain, LangGraph, DeepEval, MCP
- Vector & Retrieval: Pinecone, Weaviate, or similar
- Databases & APIs: PostgreSQL, Snowflake, RESTful API design
- Infrastructure: Docker, Kubernetes, GCP or AWS, Supabase
- Collaboration & Dev Tools: GitHub, Linear, Cursor / Claude Code
Pay Scale
- $120,000 - $150,000 CAD per year
- The final compensation within this range will be determined based on the candidate’s experience, skills, and overall fit for the role.
