Staff ML/LLM Ops Engineer

<div class="content-intro"><p><strong>ABOUT LVT</strong></p> <p><a href="https://www.lvt.com/resources/why-work-at-lvt">LVT</a> is redefining how businesses operate in the physical world, moving beyond traditional security solutions to deliver AI-driven, actionable intelligence that makes sites smarter, safer, and more secure. Since pioneering our first <a href="https://www.lvt.com/story">mobile, solar-powered units</a>, our commitment to scrappy, hands-on innovation has made us an established leader and one of the fastest-growing companies in intelligent site technology. We are building the next generation of solutions—from our physical units in the field to a powerful Agentic AI platform—that allows our customers to gain unprecedented visibility and control over safety, compliance, and operations. This is your chance to <a href="https://www.lvt.com/careers">join a cutting-edge team</a> that isn't just watching the world change, but actively building the technology that is changing it.</p> <p>We’re a team that’s focused on growth and innovation, and we’re proud that our crew, products, and leadership are being recognized for it.</p> <ul> <li><strong>A Top-Tier Growth Company: </strong>Named one of the <em>Financial Times’ Fastest Growing Companies 2025</em> and <em>#10 on the Inc. 5000 Rocky Mountain Regional</em> list for 2025.</li> <li><strong>Innovative Leadership: </strong>Our CEO, Ryan Porter, was named an <em>EY Entrepreneur of the Year 2025</em>, and our CTO, Steve Lindsey, was inducted into the <em>Silicon Slopes CTO Hall of Fame</em> in 2024.</li> <li><strong>Product &amp; Software Excellence: </strong>We were named one of <em>The Software Report’s Top 100 Software Companies of 2023</em> and are a winner of the <em>Security Today Govies Award </em>for 2025.</li> </ul></div><div class="p-rich_text_section"> <p><strong>ABOUT THIS ROLE</strong></p> <p>We are seeking a Staff ML/LLM Ops Engineer to own the model lifecycle as infrastructure that turns the path from research to production into standardized self-serve tooling. The model portfolio this platform serves spans both the computer-vision models in production today and a growing set of LLM, VLM, and agentic workloads. Bringing those generative workloads under the same lifecycle discipline: serving, version-pinning, evaluation, guardrails, and cost and latency monitoring is a part of this role's scope.</p> <p>This is a senior individual-contributor and technical-leadership role. You will partner closely with AI/ML research, the application backend team, and platform and infrastructure teams. You should be equally comfortable discussing model-serving architectures, CI/CD and rollback design, polyglot service contracts, and production observability.</p> <p>&nbsp;</p> <p><strong>ROLE RESPONSIBILITIES</strong></p> <ul> <li><strong>MLOps:</strong> Own the model lifecycle end to end: standardized packaging, a model CI/CD path, a serving layer with stable, versioned contracts, automated deployment and rollback, and monitoring and drift detection.</li> <li><strong>LLMOps:</strong> Bring LLM, VLM, and agentic workloads under the same platform discipline as the vision models serving with models and prompts version-pinned as deployable, rollback-able artifacts; generative evaluation and regression suites that don't reduce to precision/recall; production guardrails such as input/output filtering and jailbreak and refusal monitoring; and token-level cost and latency observability. Where retrieval or agent orchestration is in play, own the operational seams (vector stores, request tracing) the same way.</li> <li><strong>CI/CD:</strong> Make the path from research to production self-serve <em>and</em> safe by encoding the security, observability, and on-call guardrails engineers enforce by hand today, so model owners can ship without lowering the operational bar.</li> <li><strong>API Boundary Ownership:</strong> Define and own the contract boundary between the model platform and the application backend so engineers integrate against deployed models independently.</li> <li><strong>Technical Mentorship:</strong> Set technical standards and mentor IC productionization work toward the platform, growing the function as the team forms.</li> </ul> <p>&nbsp;</p> <p><strong>OUR IDEAL CANDIDATE</strong></p> <ul> <li><strong>MLOps &amp; Platform Experience:</strong> 8+ years of engineering experience with deep ML-infrastructure / MLOps work, including building and operating a model deployment, serving, and monitoring platform in production.</li> <li><strong>LLM Ops:</strong> Hands-on experience operating LLM or VLM workloads in production including model serving or managed-provider integration, prompt and version management, generative evaluation, guardrails, and token cost and latency control.</li> <li><strong>Self-Serve ML Deployment:</strong> Experience designing self-serve ML deployment for other teams, including model registry and packaging, CI/CD for models, serving contracts, rollback, and drift/quality monitoring.</li> <li><strong>API Design:</strong> Strong systems and API design judgment across a polyglot boundary with the operational maturity to own security, observability, and on-call trade-offs.</li> <li><strong>Technical Leadership:</strong> A track record of setting technical direction and leveling up engineers (technical leadership; formal management not required).</li> <li><strong>Education:</strong> Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.</li> </ul> <p>&nbsp;</p> <p><strong>PREFERRED QUALIFICATIONS</strong></p> <ul> <li>Computer Vision / video model inference at scale (GPU serving, latency and cost optimization).</li> <li>Cloud-native infrastructure (Kubernetes, Argo, or a comparable deployment stack).</li> <li>Experience standing up an ML platform from zero on a team that did not have one.</li> <li>Experience deploying AI models to edge environments (e.g. NVIDIA Jetson or similar).</li> <li>Agentic and generative tooling: LangGraph, MCP frameworks, vector databases, and inference/serving platforms.</li> </ul> <p>&nbsp;</p> <p><strong>COMPENSATION</strong></p> <p>The beginning annual salary range for this role is $213,300 - $272,000 USD and is determined by location, job-related experience, and education/training. Your total earning potential is amplified by a bonus structure tied to meeting goals, and you will become an owner from day one through our employee equity program.</p> </div><div class="content-conclusion"><div class="p-rich_text_section"> <p><strong>BENEFITS</strong></p> <p>We believe you do your best work when your whole life is supported. We invest in our crew’s health, families, and financial futures with a benefits package designed to support you inside and outside the office. Full-time benefits include, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits (401k match up to 4%), and flexible PTO.</p> <p><strong><em>LVT IS PROUD TO BE AN EQUAL OPPORTUNITY EMPLOYER.</em></strong><em> All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. All candidates must pass a drug screening and background check upon employment. Some roles may also require passing a federal background check and fingerprinting. Must be authorized to work in the U.S. If reasonable accommodation is needed to participate in the job application or interview process, and/or to perform essential job functions, please reach out to your recruiter.</em></p> </div></div>