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 Responsibilities
• Design 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 Experience Hands-on experience working directly with open-source/open-weight models such as:
• Llama
• Qwen
• Mistral
• Mixtral
• DeepSeek
• Gemma Candidates whose experience is limited to OpenAI, Claude, Gemini,



or similar APIs will not be considered. LLM Deployment & Inference Platforms Demonstrated experience deploying and operating LLMs using technologies such as:
• vLLM
• SGLang
• llama.cpp
• Ollama (preferred)
• Quantization techniques
• Tensor Parallelism Advanced Python Development Strong hands-on experience with:
• Python
• PyTorch
• Hugging Face Transformers
• PEFT
• Accelerate Model Fine-Tuning Experience Proven experience with:
• LoRA
• QLoRA
• Adapter-based training
• Model evaluation and benchmarking
• Prompt optimization AI Infrastructure & Platform Engineering Experience operating production environments with:
• Kubernetes
• Docker
• Linux
• NVIDIA GPU infrastructure
• Monitoring and observability platforms AI Agents & RAG Architectures Practical experience building:
• Agentic systems
• Tool-calling frameworks
• Model Context Protocol (MCP)
• Retrieval-Augmented Generation (RAG) solutions
• Multi-agent workflows Secure & Private AI Environments Experience working within:
• Self-hosted AI environments
• Private AI deployments
• Security-sensitive infrastructures
• Air-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.

📌 Senior Private AI Platform Lead (México)
🏢 Unilink Transportation
📍 México