Forward Deployed Engineer, Ecosystem
AI summary of the role
Nebius is building an ecosystem function to ensure top AI companies build on and stay with its full-stack AI cloud.
What you’ll do
- Design and prototype integrations between partner products and the Nebius platform
- Define reference architectures for partner integrations at scale and in production
- Build production-quality proof-of-concepts across agentic pipelines, RAG, inference optimization, and multi-model orchestration
- Work directly with partner engineering teams to scope, prototype, and progress integrations
What you’ll bring
- 6+ years of hands-on engineering experience in AI application development, ML systems, or AI infrastructure
- Deep working knowledge of the AI developer stack (LLM APIs, inference runtimes, orchestration frameworks, vector databases, RAG, agentic pipelines)
- Hands-on experience with agentic frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent
- Strong Python programming skills and comfort prototyping end-to-end AI systems quickly
Technologies
Python · vLLM · SGLang · TensorRT-LLM · LangChain · LangGraph · CrewAI · AutoGen · Qdrant · Weaviate · Milvus · pgvector
About Nebius
Full-stack AI cloud infrastructure platform delivering GPU compute and software to hyperscalers and enterprises without requiring in-house AI/ML teams.
Public · 1000–2000 people
Source and classification
Technical pre-sales · Evidence for this classification:
pipelines — all of it purpose-built for the most demanding AI workloads. What we are now building is the ecosystem function that ensures the best AI companies choose to build on us, integrate with us, and stay. As a Forward Deployed Engineer, Ecosystem, you will sit at the intersection of solution architecture and hands-on engineering. You assess how partner products actually work on our stack, define the reference architecture for each integration, build the working prototype that proves it, and translate what you find into product requirements that shape what Nebius ships next. Your responsibilities will include: Solutioning & Architecture Design and prototype integrations between partner products and the Nebius platform — fast, hands-on, and technically sound Define reference architectures for partner integrations — not just what works, but how it should work at scale and in
More from the job description
About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius builds the infrastructure serious AI teams run on — GPU clusters, inference runtimes, agent development environments, data pipelines — all of it purpose-built for the most demanding AI workloads. What we are now building is the ecosystem function that ensures the best AI companies choose to build on us, integrate with us, and stay. As a Forward Deployed Engineer, Ecosystem, you will sit at the intersection of solution architecture and hands-on engineering. You assess how partner products actually work on our stack, define the reference architecture for each integration, build the working prototype that proves it, and [... source excerpt omitted ...] the Nebius platform — fast, hands-on, and technically sound Define reference architectures for partner integrations — not just what works, but how it should work at scale and in production Scope partner architectures against our platform — how does this product actually work on our stack, where does it snap together, where does it break Build production-quality proof-of-concepts across the AI stack including agentic pipelines, RAG architectures, inference optimization patterns, and multi-model orchestration Produce working proof-of-concepts that serve as the starting point for product creation — not a requirements doc, a working thing Maintain a library of reference architect [... source excerpt omitted ...] es your pod partner and internal teams a clear picture of integration feasibility, depth, and complexity Internal Translate external integration findings into actionable product requirements for Nebius platform teams Work with ISV partners, SI teams, and field teams to scale solution adoption and drive revenue once a solution is ready Surface recurring architectural patterns and integration gaps to inform platform roadmap decisions Participate in platform planning as the technical voice of what you are seeing and building in the field Ecosystem Presence Represent Nebius at hackathons, in open source communities, and at technical events Build in public — demos, reference archi
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