Staff ML Ops Engineer

Job description

**ABOUT LVT** [LVT](https://www.lvt.com/resources/why-work-at-lvt) 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 [mobile, solar-powered units](https://www.lvt.com/story), 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 [join a cutting-edge team](https://www.lvt.com/careers) that isn't just watching the world change, but actively building the technology that is changing it. 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. - **A Top-Tier Growth Company:** Named one of the *Financial Times’ Fastest Growing Companies 2025* and *#10 on the Inc. 5000 Rocky Mountain Regional* list for 2025. - **Innovative Leadership:** Our CEO, Ryan Porter, was named an *EY Entrepreneur of the Year 2025*, and our CTO, Steve Lindsey, was inducted into the *Silicon Slopes CTO Hall of Fame* in 2024. - **Product & Software Excellence:** We were named one of *The Software Report’s Top 100 Software Companies of 2023* and are a winner of the *Security Today Govies Award* for 2025. **ABOUT THIS ROLE** We are seeking a Staff ML 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. 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. **ROLE RESPONSIBILITIES** - **MLOps:** 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. - **LLMOps:** 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. - **CI/CD:** Make the path from research to production self-serve *and* 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. - **API Boundary Ownership:** Define and own the contract boundary between the model platform and the application backend so engineers integrate against deployed models independently. - **Technical Mentorship:** Set technical standards and mentor IC productionization work toward the platform, growing the function as the team forms. **OUR IDEAL CANDIDATE** - **MLOps & Platform Experience:** 8+ years of engineering experience with deep ML-infrastructure / MLOps work, including building and operating a model deployment, serving, and monitoring platform in production. - **LLM Ops:** 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. - **Self-Serve ML Deployment:** 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. - **API Design:** Strong systems and API design judgment across a polyglot boundary with the operational maturity to own security, observability, and on-call trade-offs. - **Technical Leadership:** A track record of setting technical direction and leveling up engineers (technical leadership; formal management not required). - **Education:** Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. **PREFERRED QUALIFICATIONS** - Computer Vision / video model inference at scale (GPU serving, latency and cost optimization). - Cloud-native infrastructure (Kubernetes, Argo, or a comparable deployment stack). - Experience standing up an ML platform from zero on a team that did not have one. - Experience deploying AI models to edge environments (e.g. NVIDIA Jetson or similar). - Agentic and generative tooling: LangGraph, MCP frameworks, vector databases, and inference/serving platforms. **COMPENSATION** 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. **BENEFITS** 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. ***LVT IS PROUD TO BE AN EQUAL OPPORTUNITY EMPLOYER.*** *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.*