Lead DevOps Engineer

Praktika

2d•Remote

About The Position

Praktika is hiring a Lead DevOps Engineer to own the company’s infrastructure and access management. This hands-on leadership role is responsible for setting infrastructure direction, building and operating systems, and keeping them secure, reliable, cost-efficient, and fast as the company grows. The position works with engineering and non-engineering teams in a fully distributed, asynchronous, written-first environment. The role is based remotely in Dubai (UAE) or Eastern Europe. The Lead DevOps Engineer will own Praktika’s AWS infrastructure, company-wide identity and access, infrastructure security, technical compliance work, infrastructure costs, and hosting and inference infrastructure for ML and LLM models. They will automate recurring operational work, rethink infrastructure processes around AI, and create safe environments for deploying and monitoring internal tools and apps. They will also provide delivery teams with service templates, CI/CD, safe deployment practices, and alerts that make the standard approach easy without blocking teams.

Requirements

Ownership mindset: able to make decisions and complete work without supervision.
Deep hands-on AWS experience in production, including containers on ECS.
Experience using Terraform as the normal approach to infrastructure as code.
Solid security foundation and genuine interest in security.
Has built working tools with AI coding agents and uses AI in daily work.
Has changed how other teams work without formal authority over them.
Able to thrive in an asynchronous, distributed, written-first culture.

Nice To Haves

DevSecOps experience and an understanding of how attacks work.
Hands-on experience with SOC 2, ISO 27001, or GDPR.
Experience with cloud providers other than AWS.
Experience with infrastructure for ML or LLM inference.
Prior experience at an early-stage or high-growth consumer product company.

Responsibilities

Design, build, and operate the AWS-based infrastructure end to end, and take ownership when it fails.
Own company-wide identity and access, including cloud accounts, vendors, internal tools, onboarding, and offboarding.
Own infrastructure security, including IAM, networks, secrets, audit logs, and the technical aspects of ISO 27001, SOC 2, and GDPR compliance.
Keep costs visible and under control across cloud providers and AI vendors.
Run hosting and inference infrastructure for ML and LLM models.
Automate repeated operational requests and provide self-service rather than relying on tickets and manual console changes.
Rebuild infrastructure processes around AI, including code review, monitoring and observability, alert triage, and incident investigation.
Build a safe environment for internal tools and apps created with AI by engineers and non-engineers to be deployed, reviewed, and monitored.
Build tools using AI.
Provide delivery teams with service templates, CI/CD, safe deployment practices, and alerts that make the standard way of working easy without blocking teams.
Set technical direction and divide large projects into deliverable parts.