דרושים » מחשבים ורשתות » Senior DevOps Engineer

We're looking for a DevOps Engineer to establish and grow the automation backbone of our embedded engineering team. In this role you'll build the CI/CD pipelines, build systems, and test automation that let our firmware, DSP & ML engineers ship faster and with higher confidence. This is a foundational, high-impact position: you'll shape how the team develops, tests, and releases embedded software from the ground up.
Our team develops embedded software and firmware for edge devices. As the team scales, we need dedicated DevOps expertise to move us beyond manual builds and ad-hoc testing toward a fully automated, reproducible development workflow.
You'll be the go-to person for everything related to development automation - from automating cross-compilation and firmware builds, to integrating hardware-in-the-loop testing, to setting up continuous integration that catches problems before they reach hardware. You'll work closely with embedded engineers to understand their pain points and remove friction from their daily work, and partner with the team leader to define the team's automation strategy. Because this is a new function on the team, you'll have significant ownership and influence over tooling and process decisions.

Responsibilities
Design, build, and maintain CI/CD pipelines for embedded firmware and software (build, static analysis, unit/integration tests, artifact management, and release).
Automate cross-compilation and build systems to produce reproducible, versioned firmware artifacts.
Set up and maintain automated test infrastructure, including hardware-in-the-loop test benches and simulation/emulation environments.
Manage and automate the firmware release and flashing/deployment process, including version control, signing, and OTA update pipelines where applicable.
Establish and maintain infrastructure for build agents and test rigs (self-hosted runners, containerized build environments, lab hardware).
Introduce and enforce best practices for source control, branching, code review automation, and artifact/dependency management.
Improve developer experience by identifying bottlenecks in the development workflow and automating repetitive tasks.
Set up monitoring, logging, and alerting for build and test infrastructure to ensure reliability and fast feedback.
Document tooling, pipelines, and processes, and mentor engineers on automation practices.

as the it service desk team leader at the company, you will drive the end-to-end operation of our global service desk, managing our people, service quality, technical standards, and ai-driven support capabilities. reporting directly to the head of global it, you will guide a distributed group of service desk engineers spanning hq/emea, the americas, and apac.
you will serve as the operational catalyst for our transition from traditional ticket queues to an ai-native itsm framework. this modern model prioritizes higher self-service resolution, leverages a tightly governed knowledge base as a single source of truth for ai and employees alike, and integrates automated processes directly into daily workflows.
operating as a true player-coach, you will mentor the team and evaluate service performance using clear metrics, while maintaining the technical expertise needed to handle complex escalations and help architect tier-0 self-service solutions. rather than simply managing incoming tickets, your success will be defined by eliminating repetitive tasks and sharpening the team's capabilities for high-value Technical Support.
what you'll do
lead and develop the team
manage the global service desk team: hiring, onboarding, goal-setting, performance, coaching, and career development
run follow-the-sun coverage across regions, including shift planning, on-call, and handoffs between hq/emea, the americas, and apac
build a culture of ownership, service quality, and continuous learning; level up engineers through pairing, technical reviews, and knowledge transfer
define roles, responsibilities, and growth paths within the team so that technical and service ladders are explicit and understood
represent the service desk to the wider it organization, to leadership, and to business stakeholders
own service operations and outcomes
own the service desk kpis end to end: self-service rate, csat, time-to-fulfillment, sla attainment, knowledge coverage and quality, and automation hours saved
operate and continuously improve the itsm platform: queues, workflows, request catalog, automation rules, and reporting
own major incidents and vip escalations end-to-end, including stakeholder communication, coordination with infrastructure and security, and post-incident learning
drive onboarding, offboarding, and asset lifecycle quality across the employee lifecycle, in partnership with people, security, and facilities
report on service health and transformation progress to it leadership with data, not anecdotes
set the technical standard
act as the senior technical escalation lead for macos, windows, mdm systems (kandji, intune), iam (okta), google workspace, slack, av conference equipment (zoom rooms, neat, google meet), networking, and llm administration console management.
own runbooks, troubleshooting methodology, and technical standards, and ensure the team actually adopts them
partner with infrastructure, security, and it operations on cross-functional issues, access governance, and endpoint compliance
stay hands-on: take escalations, ship fixes, and keep your own technical credibility current
build the ai-native service desk (tier 0)
own the tier-0 roadmap: identify the highest-volume, lowest-value request categories and design them out of existence through self-service, knowledge, and automation
govern the knowledge base as a product: ownership, quality standards, review cycles, and structure fit for both human readers and ai grounding
build and oversee automations that remove entire ticket classes (make.com, scripting, itsm-native automation), with measured impact
develop, evaluate, and improve ai-assisted support workflows; define how the team validates ai output and handles its failure modes
make the team ai-native by default: ai fluency is a baseline for every engineer, and building with ai is a shared responsibility rather than a side project.