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Nebius is a Nasdaq-listed AI cloud infrastructure company headquartered in Amsterdam with 1,500+ employees and R&D hubs across Europe, North America, and Israel. The company builds a full-stack AI cloud platform supporting developers and enterprises from data and model training through production deployment.
This role sits within Nebius Token Factory, a serverless platform for running and customizing open-source LLMs in production. Token Factory enables serverless inference and fine-tuning (LoRA, full fine-tuning, reinforcement learning-based fine-tuning) backed by in-house optimizations including custom speculative decoding, quantization, cache-aware routing, and dedicated endpoints.
As a Senior ML Solutions Architect, you will collaborate with enterprise customers to design and implement optimized inference workflows, build customized LLM-based solutions, and architect scalable AI applications using Nebius-served models. You will work closely with the backend engineering team to improve the platform based on customer needs and feedback.
Key responsibilities:
- Optimize LLM inference across various modalities (text, vision, audio) to drive business value and support customer goals
- Provide technical support in supervised and reinforcement learning fine-tuning to maximize model quality
- Design and implement LLM-based solutions using Token Factory's serverless inference services
- Build production-ready applications leveraging serverless LLM APIs, including multimodal and domain-specific models
- Provide technical expertise in prompt engineering, RAG architectures, and model selection
- Collaborate with product and engineering teams to surface customer feedback and shape platform roadmap
- Guide customers in scaling from proof-of-concept to production with focus on performance, reliability, and cost efficiency
Requirements:
- 5+ years of experience in ML/AI systems, with at least 2 years focused on LLMs and generative AI
- Deep knowledge of the LLM ecosystem, including model architectures and fine-tuning approaches
- Hands-on production experience with: running LLMs in production (deploying and operating inference workloads); LLM fine-tuning including supervised fine-tuning (SFT/LoRA) and data preparation/curation; LLM evaluation including building task-specific benchmarks and offline/online evaluation pipelines with LLM-as-a-judge setups; inference frameworks and libraries (vLLM, SGLang, TensorRT-LLM, Transformers); deploying LLM-powered applications using APIs from OpenAI, Anthropic, or open-source models
- Experience with RL-based fine-tuning is a strong plus
- Strong Python programming skills
- Excellent communication skills with ability to explain technical concepts to diverse audiences
Bonuses:
- Experience with multimodal AI models (vision-language, speech)
- Proficiency with DevOps tools (Docker, Kubernetes)
- Contributions to open-source ML/AI projects
Preferred technical stack: Python; ML frameworks (vLLM, TensorRT-LLM, SGLang, Transformers, OpenAI/Anthropic SDKs); MLOps/DevOps tools (Kubernetes, Docker, Git); cloud platforms (AWS SageMaker/Bedrock, GCP Vertex AI, Azure ML).
