CONFIDENTIAL AI Engineer

• Partner directly with executives and senior stakeholders to translate complex business needs into practical AI strategies and technical solutions.
• Serve as a trusted technical advisor when evaluating AI opportunities, risks, architecture, infrastructure, and emerging technologies.
• Design, build, deploy, operate, and troubleshoot production AI/ML environments.
• Deploy and optimize open-source and locally hosted AI models, including models such as Llama, Mistral, Qwen, Gemma, or comparable technologies.
• Build and support AI workloads running on GPU-backed infrastructure.
• Evaluate model size, GPU memory, RAM, quantization, inference requirements, latency, throughput, and available compute when designing solutions.
• Build and maintain model-serving and inference environments.
• Take AI solutions from early experimentation through production deployment and ongoing operational support.
• Build and integrate LLM, RAG, retrieval/search, agentic AI, and workflow automation solutions.
• Develop APIs and integrations across structured and unstructured enterprise data sources.
• Containerize and orchestrate AI workloads using technologies such as Docker and Kubernetes.
• Implement monitoring, observability, evaluation, and performance testing across AI systems.
• Troubleshoot complex issues across infrastructure, model, application, integration, and data layers.
• Evaluate when hosted AI platforms make sense versus when solutions should be deployed within internally controlled infrastructure.
• Research and experiment with emerging models, inference frameworks, infrastructure, and deployment technologies.
• Work independently through ambiguous technical challenges while collaborating with both technical and nontechnical stakeholders.
• Help establish scalable, secure, and repeatable approaches to delivering AI solutions.

The right person will be equally comfortable experimenting with an open-source model, troubleshooting GPU or resource constraints, building an inference environment, writing Python, deploying containers, and figuring out a technical problem that doesn’t already have a playbook., * Significant hands-on AI Infrastructure, MLOps, platform engineering, systems engineering, or related experience.

• Experience deploying AI/ML workloads into production rather than exclusively consuming hosted AI APIs.
• Hands-on experience deploying or experimenting with open-source/local models.
• Practical experience with GPU-backed AI workloads and an understanding of:
• GPU memory / VRAM
• System RAM and capacity
• Model sizing
• Quantization
• Inference performance
• Resource constraints
• Latency and throughput
• Strong infrastructure fundamentals across areas such as Linux, servers, compute, networking, storage, memory, performance, and troubleshooting.
• Strong software engineering skills with Python or another applicable programming language.
• Experience with Docker, Kubernetes, APIs, and modern deployment practices.
• Experience with model serving/inference technologies such as vLLM, NVIDIA Triton, Hugging Face, Ollama, Ray Serve, TGI, llama.cpp, or comparable tools.
• Understanding of RAG, embeddings, vector search, information retrieval, structured/unstructured data, and model evaluation.
• Experience implementing monitoring and observability for production applications or AI systems.
• Ability to independently research unfamiliar technologies and solve problems without relying on an established playbook.
• Strong security mindset when working with confidential or sensitive data.
• Ability to communicate technical decisions, limitations, and tradeoffs to both technical and nontechnical stakeholders.

LHH is partnering with an global firm that is expanding its investment in AI and looking for a senior, highly hands-on engineer to help build the technical foundation behind its next generation of AI solutions.