Forward Deployed Engineer, Ecosystem

Details

Nebius is building a full-stack AI cloud platform supporting data and model training through production deployment. The Forward Deployed Engineer, Ecosystem sits at the intersection of solution architecture and hands-on engineering, assessing partner products on the Nebius stack, defining reference architectures, building production-quality prototypes, and translating integration findings into product requirements.

Responsibilities

Solutioning and Architecture

• Design and prototype technically sound integrations between partner products and the Nebius platform.
• Define reference architectures for partner integrations that work at scale and in production.
• Scope partner architectures against the Nebius stack, identifying integration points and limitations.
• Build proof-of-concepts across agentic pipelines, RAG architectures, inference optimization, and multi-model orchestration.
• Maintain a library of reference architectures and integration patterns.

Technical Partner Scoping

• Work directly with partner engineering teams to scope, prototype, and progress integrations.
• Assess partner architectures and report integration feasibility and complexity.
• Provide technical guidance on performance, reliability, and cost efficiency on Nebius infrastructure.
• Produce technical scoping for partners and internal teams.

Internal Collaboration

• Translate integration findings into actionable product requirements.
• Work with ISV partners, systems integrator teams, and field teams to scale solution adoption.
• Surface architectural patterns and integration gaps to inform the platform roadmap.
• Participate in platform planning as the technical voice of field experience.

Ecosystem Presence

• Represent Nebius at hackathons, open-source communities, and technical events.
• Build public demos, reference architectures, and integrations.
• Stay current with the AI tooling ecosystem.

Platform Focus Areas

• Agentic systems: agent frameworks, memory systems, tool integration, orchestration, MCP, and guardrails.
• Managed inference: inference runtimes, model serving, optimization tooling, speculative decoding, and KV-cache routing.
• IaaS and managed infrastructure: cloud-native integrations, GPU orchestration, and enterprise platform connectors.
• Data: vector databases, retrieval systems, RAG architectures, data pipeline integrations, and synthetic data tooling.

Requirements

• 6+ years of hands-on engineering experience in AI application development, ML systems, or AI infrastructure.
• Deep working knowledge of LLM APIs, inference runtimes, orchestration frameworks, vector databases, RAG architectures, and agentic pipelines.
• Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or equivalent agentic frameworks.
• Strong Python programming skills and the ability to prototype end-to-end AI systems quickly.
• Experience defining reference architectures and technical patterns.
• Proven ability to move from an idea to a working prototype under time pressure.
• Experience building integrations across APIs and developer platforms.
• Ability to work with external partner engineering teams and internal product and engineering teams.
• Strong technical communication skills.

Additional Qualifications

• Experience with vLLM, SGLang, TensorRT-LLM, speculative decoding, quantization, batching, or KV-cache routing.
• Familiarity with NVIDIA CUDA, TensorRT, NeMo, or equivalent.
• Experience with multimodal AI models.
• AI hackathon participation or success.
• Experience as a developer advocate, solutions engineer, or technical partner manager at an AI platform or developer tooling company.
• Early engineering experience at an AI startup.
• Open-source projects or public demos with meaningful community adoption.
• Proficiency with Docker, Kubernetes, and Git.

Preferred Technical Stack

• Python
• vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI SDKs, and Anthropic SDKs
• LangChain, LangGraph, CrewAI, AutoGen, and smolagents
• Qdrant, Weaviate, Milvus, and pgvector
• FastAPI and Flask
• Kubernetes, Docker, and Git
• AWS, Google Cloud Platform, and Microsoft Azure

Benefits

• 100% company-paid medical, dental, and vision coverage for employees and families.
• 401(k) plan with up to a 4% company match and immediate vesting.
• 20 weeks of paid parental leave for primary caregivers and 12 weeks for secondary caregivers.
• Up to $85 per month for mobile and internet expenses.
• Company-paid short-term, long-term, and life insurance.
• Career growth and learning opportunities, flexibility and ownership, and the opportunity to work on impactful AI projects.
• Applicants must be authorized to work in the country in which they apply and must provide proof of employment eligibility.