Staff ML/LLM Ops Engineer

LVT•Seattle, WA

About The Position

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. 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.

Requirements

8+ years of engineering experience with deep ML-infrastructure / MLOps work, including building and operating a model deployment, serving, and monitoring platform in production.
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.
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.
Strong systems and API design judgment across a polyglot boundary with the operational maturity to own security, observability, and on-call trade-offs.
A track record of setting technical direction and leveling up engineers (technical leadership; formal management not required).
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

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.

Responsibilities

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.
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.
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.
Define and own the contract boundary between the model platform and the application backend so engineers integrate against deployed models independently.
Set technical standards and mentor IC productionization work toward the platform, growing the function as the team forms.