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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal ML Solutions Architect - Token Factory - **Company:** LC Manufacturing, LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $208,000.0 - $261,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Cleansing, Software Debugging, DevOps, High-Level Architecture, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Tensorflow, Azure Machine Learning, DevOps Tools - Open-source, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Backend, Git, Kubernetes, Machine Learning Operations, TensorRT, Serverless Computing, Docker - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/principal-ml-solutions-architect-token-factory-nebius-8647873 ## About the Role * 8+ years of experience in ML/AI systems, with at least 4 years focused on LLMs and generative AI * Demonstrated technical leadership: owning ambiguous, high-impact problems end to end and influencing decisions across teams and customers * Expert knowledge of the LLM ecosystem: model architectures, fine-tuning approaches, and inference internals * Deep, hands-on command of inference optimization: quantization, KV-cache management, batching, routing, etc. * Hands-on experience with: + Running LLMs in production at scale: deploying, operating, and debugging inference workloads down to the framework level + LLM fine-tuning, including SFT/LoRA and data preparation/curation; experience with RL-based fine-tuning + LLM evaluation: building task-specific benchmarks and offline/online eval pipelines, including LLM-as-a-judge setups + Inference frameworks and libraries (vLLM, SGLang, TensorRT-LLM), including the ability to read, modify, and contribute to their internals + Deploying LLM-powered applications using APIs from OpenAI, Anthropic, or open-source models * Strong Python programming skills * Excellent communication skills, with the ability to clearly explain technical concepts to diverse audiences, from engineers to executives It would be an added bonus if you have: * Contributions or maintainership in major OSS inference/ML projects (vLLM, SGLang, TensorRT-LLM) * Published research, conference talks, or widely-read technical writing in the LLM/serving space * Deep work with multimodal AI models (vision-language, speech) * Proficiency with DevOps tooling (Docker, Kubernetes) and infrastructure-as-code * Experience building or owning internal tooling/automation for ML workflows at scale Preferred technical stack: * Programming Languages: Python * ML Frameworks and Libraries: vLLM, TensorRT-LLM, SGLang, Transformers, OpenAI/Anthropic SDKs * MLOps and DevOps tools: Kubernetes (K8s), Docker, Git * Cloud Platforms: AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML), Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. ## Description This position sits within Nebius Token Factory, our serverless platform for running and customizing open-source LLMs in production. Token Factory allows for serverless inference and fine-tuning (LoRA, full FT, RFT) backed by in-house optimizations like custom speculative decoding, quantization, cache-aware routing and dedicated endpoints. Customers come to us to move from prototype to scaled production without the cost and complexity of building and tuning their own inference stack., We're looking for a Principal ML Solutions Architect to act as the most senior technical authority for customers leveraging Token Factory's serverless inference and fine-tuning platforms. Beyond designing and implementing optimized inference and fine-tuning workflows, you will set technical direction across our largest and most strategic accounts, own the hardest performance and quality problems end to end, mentor other Solutions Architects, and serve as a primary technical voice shaping the platform roadmap with backend, product, and research teams., * Own the most complex, highest-stakes customer engagements from architecture through production across multiple modalities, driving measurable business value * Optimize LLM inference at the framework and hardware level and codify the resulting best practices into reusable playbooks for the team * Lead supervised and reinforcement fine-tuning efforts to maximize model quality * Design and implement production-ready LLM solutions using Token Factory's inference services * Provide deep technical expertise in prompt engineering, RAG architectures, model selection, and cost/performance trade-offs at scale * Partner closely with product, engineering and research to surface customer needs, prototype platform features, and directly influence the roadmap * Guide customers from PoC to production with a focus on performance, reliability, and cost efficiency - and define the standards by which the team does so * Mentor Senior and mid-level Solutions Architects; raise the technical bar of the team through review, enablement, and knowledge sharing * Represent Token Factory externally through talks, blog posts, and conferences ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market)