Staff ML/LLM Ops Engineer

About this role

ABOUT LVT

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, 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 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/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.

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…