Jobs at LVT

Lead end-to-end ML data engineering: build and own pipelines that convert raw telemetry and video into versioned, governed training and benchmark datasets. Design schema, storage, dataset versioning, governance, reader/writer libraries, and integrations for researchers; partner with annotation, edge, and MLOps teams and provide technical mentorship.

Lead end-to-end model lifecycle as infrastructure: standardized packaging, model CI/CD, serving with versioned contracts, automated deployment/rollback, monitoring and drift detection. Extend platform discipline to LLM/VLM/agentic workloads (prompt/versioning, generative evaluation, guardrails, token cost/latency observability). Define API boundaries, enable self-serve safe deployments, own operational seams (vector stores, tracing), and mentor engineers to grow the ML/LLM ops function.

Own end-to-end delivery of a GenAI-powered security deterrence product: design services and APIs, integrate detection models with prompting and voice/TTS, build evaluation and regression suites, ensure precision and safety for real-world actuations, and drive build-vs-buy decisions while collaborating across platform, data, and ML teams.

Build and harden the cloud backbone for an agentic AI platform: scale and ensure reliability of the event pipeline, dispatch to agent/CV workflows, maintain projections and context stores, and own observability, SLOs, and operational readiness at fleet scale.

Lead and grow an engineering team building Agentic AI platforms and distributed edge-to-cloud systems. Drive architecture, AI model operationalization, orchestration, scalability, reliability, MLOps, and cross-functional delivery while hiring, mentoring, and setting technical strategy.

Design, build, and automate scalable, secure AWS infrastructure for AI and IoT systems. Implement IaC with Terraform, manage Kubernetes clusters, develop CI/CD via GitHub Actions and ArgoCD, engineer complex networking, enhance observability, build internal tooling, and lead technical reviews while mentoring junior engineers.

Lead product marketing to define corporate narrative, pricing, and positioning for AI-driven hardware and software. Influence sales, conduct market and competitive research, align product roadmap with market needs, manage two product marketers, and build scalable processes and enablement for complex B2B deals.

Lead strategic vision for Intelligence and Ecosystem product management, integrating AI and analytics while ensuring user experience and compliance with security standards.

Design, scale, and secure cloud infrastructure and platform solutions for a global IoT edge fleet and Agentic AI platform. Own IaC, manage high-availability Kubernetes clusters, drive GitOps deployment automation, enforce zero-trust and secrets management, implement observability and alerting, optimize cloud cost and operations, and produce documentation, runbooks, and architecture artifacts while collaborating across software and hardware teams.

Build and maintain full-stack TypeScript features across React/Next.js front end and Node.js back end. Develop and integrate REST and GraphQL APIs, write tests, debug issues, participate in Agile processes and code reviews, and monitor performance using observability tools.

Design, build, test, and ship full-stack TypeScript features from React/Next.js UIs to Node.js backend services. Maintain GraphQL federation, REST APIs, and real-time WebSocket flows. Diagnose cross-service bugs, enforce data contracts, instrument metrics, mentor engineers, and improve CI/CD, type safety, and legacy system deprecation to ensure reliable, scalable enterprise IoT applications.

The Strategic Account Manager will expand LVT's federal business by securing contracts, managing relationships, leading negotiations, and collaborating on solutions for federal agencies.

The Director of Public Sector Capture will lead federal contract acquisition efforts, manage pipeline development, and engage with federal programs to increase winning proposals for AI-driven solutions.

Build and scale a Value Engineering practice for GTM: create ROI, TCO, and financial models; enable sales and customer success with value-selling methodologies; co-lead executive-level conversations; standardize execution; train field teams; measure impact on deal metrics and support retention and expansion.