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Percepta

Cloud-based video analytics for shoplifting detection

AI Infrastructure Engineer

Platform Engineering +1Confirmed live in the last 24 hours
Full-timeNew York, NY, USA

About the Job

Requirements

  • At least 5 years of experience operating production infrastructure in site reliability engineering, platform engineering, or production engineering.
  • Experience operating software in environments outside one's control, such as bring-your-own-cloud, single-tenant, on-premises, or air-gapped environments.
  • Experience with AWS, Azure, and Google Cloud Platform, including networking, identity and access management, and managed Kubernetes; deep expertise in one cloud and operational familiarity with the other two.
  • Production Kubernetes experience on managed clusters across multiple cloud providers, and judgment about choosing cloud-native versus portable services.
  • Deep Terraform experience, including module design, state management, testing, drift management, and assessing blast radius.
  • Experience carrying an on-call pager, incident command, postmortems, and service-level objectives that influence operational behavior.
  • Experience working in regulated environments such as HIPAA, SOC 2, PCI, or FedRAMP, with infrastructure-based controls.
  • Comfort working directly with customers in architecture reviews and with customer network teams on firewall exceptions.
  • Proficiency in Python, Go, or Bash, and ability to automate repeatable processes.
  • Experience operating infrastructure through a vendor control plane such as Ryvn, Nuon, or Replicated.
  • Experience with GitOps and progressive delivery across tenants running different versions.
  • Multi-region infrastructure experience and judgment about provider-native versus portable options in a bring-your-own-cloud product.
  • Deep experience with the Grafana stack, including Mimir, Loki, and Tempo at multi-tenant scale.
  • GPU and inference operations experience, including Ray, SkyPilot, vLLM or SGLang, large language model gateways, and cost attribution.
  • Experience with machine learning operations or research-to-production handoffs.
  • Understanding of observability for non-deterministic systems and blast-radius controls for autonomous systems.

Responsibilities

  • Build operational reliability practices, including service-level objectives, alert routing, an on-call rotation, incident response, and postmortems that lead to changes.
  • Own and evolve infrastructure as code using Terraform across AWS, Azure, and Google Cloud Platform, including blueprints and per-environment installations; add tests, policy as code, and drift management.
  • Automate customer-environment setup, including firewall exceptions, DNS delegations, deployment identities, tag policies, and certificate chains.
  • Maintain service parity across AWS, Azure, and Google Cloud Platform.
  • Implement HIPAA and SOC 2 controls in infrastructure code and generate compliance evidence through the deployment pipeline.
  • Own and improve the existing observability stack—Grafana, Loki, Mimir, Tempo, Alloy, Langfuse, and LiteLLM—to answer operational questions about agent decisions, data, and costs.
  • Provide engineers deploying into customer tenants with a paved deployment path that does not require knowledge of the blueprint template engine.

Desired Qualifications

  • Experience operating infrastructure through a vendor control plane such as Ryvn, Nuon, Replicated, or similar, and informed views on the tradeoffs.
  • Experience with GitOps and progressive delivery across a fleet of tenants running different versions.
  • Multi-region experience and a perspective on when provider-native services are preferable to portable services in a bring-your-own-cloud product.
  • Deep experience with the Grafana stack, including Mimir, Loki, and Tempo at multi-tenant scale.
  • GPU and inference operations experience involving Ray, SkyPilot, vLLM or SGLang, large language model gateways, and cost attribution.
  • Machine learning operations or research-to-production handoff experience.
  • Understanding of observability for non-deterministic systems and blast-radius controls for autonomous systems.

About the Company

Percepta

Cloud-based video analytics for shoplifting detection

Percepta Labs builds cloud-based video analytics for retail loss prevention. It ingests retailers’ existing security camera feeds and uses action recognition and pose estimation to identify behaviors that could indicate shoplifting in real time, then assigns a probability that a theft is happening. The system emphasizes ethical AI by anonymizing demographic features (race, gender, age) to avoid bias and protect privacy. The product is sold to retailers to help reduce shrinkage and improve margins, with early testing claiming higher accuracy than human loss-prevention staff. Percepta’s growth has been supported by a major investment from ADT Commercial, which acquired the company and appointed its co-founder as ADT’s first Director of Artificial Intelligence. The company’s goal is to provide scalable, privacy-conscious video analytics that help retailers prevent theft more efficiently and cost-effectively.

Company size
1-10
Company stage
Seed
Total funding
$1M
Headquarters
New York City, New York
Founded
2019

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