Senior Private AI Platform Lead

About the Role
We are seeking a highly experienced Senior Private AI Platform Lead to design, build, and operate a secure enterprise AI platform powered by open-weight Large Language Models (LLMs) deployed entirely within company-controlled infrastructure.This role is responsible for enabling enterprise AI capabilities without reliance on external SaaS AI providers, leading the deployment, optimization, governance, and operation of self-hosted LLMs, AI agents, and inference platforms in highly secure environments.
Key ResponsibilitiesDesign and lead the architecture of a private enterprise AI platform.Deploy, optimize, and operate open-weight LLMs in company-owned infrastructure.Build and support AI solutions using agents, RAG architectures, and tool-calling frameworks.Implement scalable inference and model-serving environments.Partner with Infrastructure, Security, and Enterprise Architecture teams.Manage AI workloads running on Kubernetes and GPU-based environments.Establish observability, monitoring, governance, and operational standards for AI systems.Drive AI Platform Engineering and MLOps best practices across the organization.
Required Qualifications (Must-Haves)Open-Weight LLM ExperienceHands-on experience working directly with open-source/open-weight models such as:LlamaQwenMistralMixtralDeepSeekGemmaCandidates whose experience is limited to OpenAI, Claude, Gemini, or similar APIs will not be considered.
LLM Deployment & Inference PlatformsDemonstrated experience deploying and operating LLMs using technologies such as:vLLMSGLangllama.cppOllama (preferred)Quantization techniquesTensor Parallelism
Advanced Python DevelopmentStrong hands-on experience with:PythonPyTorchHugging Face TransformersPEFTAccelerate
Model Fine-Tuning ExperienceProven experience with:LoRAQLoRAAdapter-based trainingModel evaluation and benchmarkingPrompt optimization
AI Infrastructure & Platform EngineeringExperience operating production environments with:KubernetesDockerLinuxNVIDIA GPU infrastructureMonitoring and observability platforms
AI Agents & RAG ArchitecturesPractical experience building:Agentic systemsTool-calling frameworksModel Context Protocol (MCP)Retrieval-Augmented Generation (RAG) solutionsMulti-agent workflows
Secure & Private AI EnvironmentsExperience working within:Self-hosted AI environmentsPrivate AI deploymentsSecurity-sensitive infrastructuresAir-gapped or offline environments (strongly preferred)
We are seeking a hands-on technical leader who has built, deployed, and operated private AI platforms using open-source LLMs in enterprise environments.