AI Platform Engineer, Agent Systems IRC296274

GlobalLogic; Town of Poland (NY); On-site; USD 100,000 - 140,000; Full time

Benefits offered by this job

Comprehensive benefits package

Flexible opportunities for work-life balance

Culture of continuous learning and development

Job summary

GlobalLogic is looking for a Software Product Engineer to lead the design of a music production agent platform. You will oversee tool interfaces, orchestration and control flow, as well as context and memory systems to enhance the functionality of AI agents in music production.

This role requires a minimum of 5 years in producing platform or infrastructure software and includes responsibilities like defining tool capabilities, designing the harness, and managing agent coherence across creative workflows.

Qualifications

• 5-10 years of experience in shipping production platform or infrastructure software.
• 18+ months building LLM agent systems, knowledge of orchestration loops and context management.
• Experience designing tool interfaces for LLM consumption.

Responsibilities

• Define how the engine’s capabilities are exposed to LLMs.
• Design and build the harness covering orchestration and control flow.
• Create systems for context, memory, and state management.

Skills

Agentic & Multi-Agent Systems

Claude code

Infrastructure

Langgraph

LLM

MCP

Orchestration

Python

TypeScript

Job description

Function: Software Product Engineering

Experience: 5-10 years

Location: Poland

Skills: Agentic & Multi-Agent Systems, Claude code, Infrastructure, Langgraph, LLM, MCP, Orchestration, Python, TypeScript

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.

#LI-OM1

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

Empowering Projects: With 500+ clients spanning diverse industries and domains, we provide an exciting opportunity to contribute to groundbreaking projects that leverage cutting‑edge technologies. As a team, we engineer digital products that positively impact people’s lives.

Empowering Growth: We foster a culture of continuous learning and professional development. Our dedication is to provide timely and comprehensive assistance for every consultant through our dedicated Learning & Development team, ensuring their continuous growth and success.

DE&I Matters: At GlobalLogic, we deeply value and embrace diversity. We are dedicated to providing equal opportunities for all individuals, fostering an inclusive and empowering work environment.

Career Development: Our corporate culture places a strong emphasis on career development, offering abundant opportunities for growth. Regular interactions with our teams ensure their engagement, motivation, and recognition. We empower our team members to pursue their career goals with confidence and enthusiasm.

Comprehensive Benefits: In addition to equitable compensation, we provide a comprehensive benefits package that prioritizes the overall well‑being of our consultants. We genuinely care about their health and strive to create a positive work environment.

Flexible Opportunities: At GlobalLogic, we prioritize work‑life balance by offering flexible opportunities tailored to your lifestyle. Explore relocation and rotation options for diverse cultural and professional experiences in different countries with our company.