Job SummaryThis 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.
ResponsibilitiesAgent 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 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
Pay Scale- $190,000 - 210,000 / 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.
