Senior AI Engineer

Location: Fully remote – United States

Salary: $120,000 - $130,000 base salary plus bonus and benefits

Travel: Up to 15%Employment: Permanent, full-time

The opportunity: We are working confidentially with an innovative US technology organisation that creates immersive, AI-powered training and simulation products for customers operating in secure and high-stakes environments. They are looking for a hands-on Senior AI Engineer to take ownership of building and deploying generative AI systems on private, locally managed GPU infrastructure. This is not a role focused solely on consuming third-party APIs or connecting applications to hosted models. You will be responsible for building AI solutions that can operate securely and independently within on-premise and air-gapped environments. You will work closely with senior technical leadership, backend engineers, real-time development teams and product specialists to take AI solutions from early prototype through to production deployment.

What you’ll be doing: Develop and maintain an on-premise LLM technology stack. Evaluate and select models based on performance, hardware and product requirements. Deploy, optimise and manage models across local GPU infrastructure. Apply quantisation and inference optimisation techniques. Build production RAG pipelines against specialist and proprietary data. Design retrieval, chunking and evaluation strategies that improve accuracy. Establish practical methods for measuring and reducing hallucinations. Build secure AI solutions capable of operating without cloud connectivity. Develop local speech pipelines covering automatic speech recognition and text-to-speech. Optimise AI systems for latency, natural interaction and concurrent users. Create integration layers between AI models and wider software products. Support real-time interactive, training and simulation experiences. Help define AI engineering standards, governance and responsible-use practices. Work directly with technical leadership to shape the organisation’s wider AI strategy.

What we’re looking for: Approximately 3–5 + years of experience across machine learning, AI engineering, automation or technical scripting. At least 1–2 years of recent hands-on generative AI experience. Proven experience deploying and managing a production on-premise or air-gapped LLM system. Strong understanding of local model deployment, model selection, quantisation and inference optimisation. Experience with inference frameworks such as vLLM, llama.cpp, TGI or comparable technologies. Practical experience managing AI workloads on local GPU infrastructure. Production experience building RAG systems against custom data. Knowledge of retrieval evaluation, prompt design, chunking and hallucination measurement. Strong Python and software-engineering fundamentals. A builder’s mentality and the ability to prototype and solve complex technical problems personally. Cloud-only AI experience will not be sufficient for this position. Desirable experience Fine-tuning or training machine-learning and generative-AI models. Local ASR and TTS technologies, including platforms such as Whisper or comparable open-source tooling. Stable Diffusion or other generative media technologies. Multi-tenant LLM architectures. Real-time applications, simulation platforms or game-engine-adjacent products. Secure deployments within defence, education or other regulated environments. Experience supporting products serving multiple simultaneous AI interactions. Why join? Take ownership of a technically ambitious AI platform. Work directly with experienced, hands-on technology leadership. Build genuine private AI infrastructure rather than API-only integrations. Deliver AI systems used in secure, real-world training and simulation environments. Influence technical architecture, standards and longer-term AI strategy. Fully remote working with occasional travel for project installations. Benefits include medical, dental and vision insurance, 401(k) and bonus eligibility. If you have personally built and deployed production LLM solutions on private GPU infrastructure, I would be keen to hear about what you built, the models and inference stack you selected, and how you approached performance, security and accuracy.