Senior DevOps Engineer

Posted 2 Days Ago
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Netanya, ISR
Hybrid
Senior level
Artificial Intelligence • Software
The Role
Architect and build a Kubernetes-native self-service platform on GCP: create controllers/operators, enforce everything-as-code (Terraform/Helm/GitOps), own production reliability with observability, SLOs and alerting, and enable secure, automated developer workflows.
Summary Generated by Built In
Description

Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul.

Fetcherr is scaling rapidly, and we are transforming our internal infrastructure into a high-velocity, self-service product. This is a senior opportunity to architect the future of our platform, setting the technical standard for how we deliver services while ensuring reliability and developer independence across diverse global markets.

What you'll do

  • Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
  • Architectural modularization: Design Kubernetes-native abstractions — controllers and operators — and decouple infrastructure into independent service lifecycles that support rapid iteration.
  • Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software — versioned, reviewed, tested, and CI-gated.
  • Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.
Requirements
  • Strong GCP experience at production scale (GKE, IAM, networking, Cloud SQL/BigQuery). No compromise here.
  • Kubernetes-native mindset: declarative-first, comfortable with controllers and operators, and a platform-engineering instinct for self-service and separation of concerns.
  • Everything-as-code discipline: any code ships through a defined SDLC (versioned, reviewed, tested, CI-gated). Terraform (KCC a plus), Helm, GitOps (ArgoCD or similar) in your toolkit.
  • Software engineering background, or proven strong development ability in Go and/or Python — you write production code, not just glue scripts.
  • Security awareness baked into how you build: least-privilege IAM, secret hygiene, policy and supply-chain gates, not bolted on after.
  • Production-first mindset: you design for reliability, observability, and recoverability, and you own what you ship.

Nice to have

  • Policy-as-code (OPA/Conftest, Kyverno) and supply-chain/CI security tooling.
  • SLO/error-budget practice and DORA-based delivery measurement.
  • Multicloud experience (AWS / Azure) — GCP is home, but breadth helps.
  • Big Data or MLOps exposure (Airflow, Dagster, Ray, Kubeflow).
  • A point of view on AI-assisted and spec-driven development.

Skills Required

  • Strong GCP production experience (GKE, IAM, networking, Cloud SQL, BigQuery)
  • Kubernetes-native mindset with experience building/controllers and operators and platform-engineering for self-service
  • Everything-as-code discipline: Terraform, Helm, GitOps (ArgoCD or similar); CI-gated SDLC for infra and config
  • Software engineering background or proven development ability in Go and/or Python
  • Security-minded engineering: least-privilege IAM, secret hygiene, policy and supply-chain gates
  • Production-first mindset: design for reliability, observability, recoverability; own what you ship
  • KCC (Kubernetes Config Connector)
  • Policy-as-code (OPA/Conftest, Kyverno) and supply-chain/CI security tooling
  • SLO/error-budget practice and DORA-based delivery measurement
  • Multicloud experience (AWS / Azure)
  • Big Data or MLOps exposure (Airflow, Dagster, Ray, Kubeflow)
  • A point of view on AI-assisted and spec-driven development
Am I A Good Fit?
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The Company
200 Employees
Year Founded: 2019

What We Do

Fetcherr is an algo-based company that revolutionizes the travel industry with its groundbreaking Generative Pricing Engine (GPE), the first of its kind to leverage AI for real-time, market-responsive pricing decisions. Our GPE augments airlines' existing pricing strategies with ultra-granular, high-frequency adjustments, fully automating workflows from pricing determination to fare publishing. Operating non-stop, the GPE identifies untapped revenue opportunities and efficiently distributes updated fares across all channels. Partnerships with Virgin Atlantic, Azul, ATPCO, and INFARE attest to our system's unparalleled capability to enhance revenue while streamlining operations.

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