Senior DevOps Engineer

present of AI; New York (NY); On-site; USD 150,000 - 200,000; Full time

Job summary

Encord is seeking an experienced DevOps/SRE responsible for the data platform's production operations. You will own deployment pipelines, cloud infrastructure across two providers, and Kubernetes clusters, with a focus on reliability and observability.

You will coach engineers in operational best practices, lead post-incident reviews, and define reliability targets that balance cost and performance for large data workloads.

Qualifications

• Hands-on production operations experience.
• Treat infrastructure as code as the normal way to build.
• Ability to set reliability targets and lead post-incident reviews.

Responsibilities

• Own deployment pipelines and cloud infrastructure.
• Manage Kubernetes clusters and multi-provider setup.
• Lead calm reviews after incidents and coach engineers on operational habits.
• Develop and enforce reliability targets across services.

Skills

Production DevOps

SRE

Observability

Tools

Terraform

Pulumi

Kubernetes

AWS

GCP

Monitoring stacks

Job description

The role owns the plumbing that keeps a data platform running: deployment pipelines, cloud infrastructure on two providers, Kubernetes clusters, and the monitoring that shows when something breaks. A strong candidate has years of hands on production operations work, treats infrastructure as code as the normal way to build, and can set reliability targets and lead calm reviews after incidents. Comfort with very large data workloads and with coaching other engineers on good operational habits matters more than knowing every tool listed.

How it fits the AI era

AI systems that train on video and sensor data put heavy, uneven load on storage and compute, so the infrastructure work here is close to the product rather than a side function. Automation and AI assisted tooling can take over routine pipeline and alert triage, which shifts the human work toward design, capacity judgment, and deciding what reliability actually means for each service. The judgment calls during an outage and the trust built with product teams stay with people.

How it builds AI experience

Working here means operating the data layer that physical AI teams depend on, so the experience gained is in running large scale pipelines for video and sensor data under real production pressure. That includes observability across many services, cost and capacity planning for heavy workloads, and reliability practices that AI teams notice. Building this starts with hands on work in cloud infrastructure, Kubernetes, and monitoring, plus a habit of measuring systems before changing them.

Worth showing

• Production DevOps or SRE
• Terraform or Pulumi
• Kubernetes on GCP or AWS
• Metrics logs traces alerting

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Encord

A data platform that helps AI teams manage, label and clean the video, sensor and image data that trains robots, self-driving cars and other physical AI systems.