Description
The company is building the agent platform for professional music production: the orchestration layer, tool interfaces, skills runtime, and context architecture that allow any AI agent to reason about and act on a music-production workflow.
You will lead the design of the orchestration loop, define how the engine’s capabilities are exposed to models, build the skills runtime that transforms a general-purpose model into a domain specialist, and architect the context and memory systems that keep agents coherent across long creative sessions.
The object model is a song. The users are producers, musicians, and creatives. The domain has real-time constraints, deep semantics, and no existing playbook.
Requirements
- Five or more years shipping production platform or infrastructure software that other engineers have built on top of.
- Eighteen or more months of production experience building LLM agent systems, covering orchestration loops, tool use, and context management. We have no preference for a specific framework. We are equally interested in engineers who shipped on a provider-agnostic framework such as LangGraph and engineers who rejected frameworks entirely and built their own harness, provided you can articulate what you learned from the path you took.
- Demonstrated experience designing tool interfaces for LLM consumption. You can explain what makes a tool schema discoverable and usable by a model versus merely technically correct.
- Demonstrated experience building context, memory, or state-management systems beyond framework defaults, including compaction, durable memory, or session persistence. You have diagnosed agent failures from raw execution traces and made targeted harness changes in response.
- Strong proficiency in TypeScript and Python.
- Experience with the Model Context Protocol (MCP) or similar tool-connectivity standards.
Nice to have (not required)
- Background in music production, audio engineering, or another creative-tool domain, including as a serious hobbyist.
- Experience with real-time audio systems, professional audio software, or other latency-sensitive environments.
- Experience making a complex desktop or professional application agent-accessible, in any domain with a rich object model (DAW, IDE, design tool, CAD).
- Experience building middleware or hook architectures that allow others to customize agent behavior without modifying core code.
Job responsibilities
What You Will Own
- Tool interfaces. Define how the engine’s capabilities are exposed to LLMs as structured, discoverable tools. This includes schemas, semantic descriptions, scoped tool sets, input validation, and output parsing that a model can reliably produce and the harness can reliably consume. Designing a tool surface that models use well is a distinct discipline from designing an API for human developers, and you will own that discipline.
- Orchestration and control flow. Design and build the harness: the core loop and the machinery around it. This covers step sequencing, retries, timeouts, error recovery, fallback paths, and multi-agent coordination where a workflow is split across sub-agents with their own tools and context. You will evaluate whether to build this in-house, adopt a framework, or extend an existing one. We have no commitment to any specific framework, and we will not build the platform on top of a single provider or model. A well-reasoned argument for building our own harness is a welcome outcome of that evaluation.
- Skills runtime. Design the format, packaging, loading, and execution layer for the structured domain knowledge that turns a generic model into a music-production specialist. This is our most distinctive platform primitive and it is largely greenfield.
- Context, memory, and state. Build the systems that keep agents performant and coherent across long, multi-step creative workflows. This includes context compaction, short-term working memory, durable cross-session memory, session state persistence, continuity across disconnects, and sub-agent delegation in which parent and child contexts remain consistent.
- Extension points. Design the harness so that new tools, skills, and middleware can be added without modifying the core runtime. Extensibility is an architectural property of the system, not a retrofit.
- Evaluations, observability, and failure analysis. Evaluations tell us the harness is working; raw execution traces and structured failure logs tell us why it is not. You will build and own the platform-level evaluation surface, the observability that every engineer on the platform depends on, and the feedback loop that converts failed agent runs into targeted harness changes.
- Ongoing simplification. As frontier models improve, some of the scaffolding we build today will stop earning its keep. You will audit the harness on a regular basis and remove the components that models no longer require.
This Role Is Not
- LLM integration engineering. This role is not responsible for wiring models to the DAW or building end-user AI features. This role builds the platform those features run on.
- ML or model engineering. This team does not train models. It builds the systems that agents run on.
- Research. This team applies current research in production. Original research happens elsewhere in the company.
What we offer
Culture of caring. At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people first. From day one, you’ll experience an inclusive culture of acceptance and belonging, where you’ll have the chance to build meaningful connections with collaborative teammates, supportive managers, and compassionate leaders.
Learning and development. We are committed to your continuous learning and development. You’ll learn and grow daily in an environment with many opportunities to try new things, sharpen your skills, and advance your career at GlobalLogic. With our Career Navigator tool as just one example, GlobalLogic offers a rich array of programs, training curricula, and hands-on opportunities to grow personally and professionally.
Interesting & meaningful work. GlobalLogic is known for engineering impact for and with clients around the world. As part of our team, you’ll have the chance to work on projects that matter. Each is a unique opportunity to engage your curiosity and creative problem-solving skills as you help clients reimagine what’s possible and bring new solutions to market. In the process, you’ll have the privilege of working on some of the most cutting-edge and impactful solutions shaping the world today.
Balance and flexibility. We believe in the importance of balance and flexibility. With many functional career areas, roles, and work arrangements, you can explore ways of achieving the perfect balance between your work and life. Your life extends beyond the office, and we always do our best to help you integrate and balance the best of work and life, having fun along the way!
High-trust organization. We are a high-trust organization where integrity is key. By joining GlobalLogic, you’re placing your trust in a safe, reliable, and ethical global company. Integrity and trust are a cornerstone of our value proposition to our employees and clients. You will find truthfulness, candor, and integrity in everything we do.
About GlobalLogic
GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world’s largest and most forward-thinking companies. Since 2000, we’ve been at the forefront of the digital revolution – helping create some of the most innovative and widely used digital products and experiences. Today we continue to collaborate with clients in transforming businesses and redefining industries through intelligent products, platforms, and services.
