Full Stack AI Engineer

Create Music Group

1d•$120,000 - $150,000

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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
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