# Senior Backend Engineer - Shelfview at Scandit

Scandit is hiring a Senior Backend Engineer - Shelfview in Tampere, Finland. Level rates it Little AI ●○○○; you can [apply on Scandit](https://jobsbylevel.com/go/a3182760-200a-4bc1-bd73-61a92a33aa49?utm_medium=ai-agent).

AI Level 1, AI centrality 6 out of 100. Tampere.

## Details

- Company: [Scandit](https://jobsbylevel.com/companies/scandit)
- AI level: AI Level 1 (score 6 out of 100)
- Location: Tampere
- Posted: September 4, 2026
- Apply: https://jobsbylevel.com/go/a3182760-200a-4bc1-bd73-61a92a33aa49?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-backend-engineer-shelfview-at-scandit-e1ca50

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## Description

Scandit Tampere Senior Backend Engineer Imagine the What. Build the How. Scandit gives people superpowers. Whether enabling delivery drivers to make quicker deliveries, matching a patient with their medication or allowing retailers to make store operations more efficient, our technology automates workflows and provides actionable insights to help businesses in a variety of industries. Join us, as we continue to expand, grow and innovate, and help take Scandit to the next level. Our newest product, ShelfView, is a machine-learning-powered platform for retail that gives real-time shelf visibility and helps stores run more efficiently. ShelfView is scaling fast: we are ramping towards the rollout of the largest retail intelligent deployments, processing billions of product-location updates a year. As a Senior Backend Engineer, you’ll be part of the team that builds and runs the backend behind ShelfView's product recognition and store-monitoring pipeline.You will help scale automated, ML-driven store monitoring while keeping the platform fast, observable, and secure for a growing list of enterprise retail customers. If these challenges sound interesting to you, we'd love to hear from you! About the Role This role is about solving complex engineering problems at scale and building the backend solutions that power them. You will help design and operate the distributed systems that turn store imagery into real-time, actionable alerts. That includes choosing and building on workflow-orchestration infrastructure for ML pipelines, making infrastructure and GPU-capacity tradeoffs for model serving at scale, and extending our identity and multi-tenancy platform as we bring on larger, more security-conscious customers. You will also help the team see what is happening in production through better observability, and mentor other engineers along the way. What You Will Do Bring the platform to the next level: load- and stress-test it against our largest deployments yet, and close the gaps that surface Push our infrastructure and GPU capacity strategy further to support larger, more demanding deployments Collaborate closely with AI/ML researchers/engineers to put their innovations to production Extend and harden our multi-tenancy platform for new customers and regions Deepen observability so the team can find and fix production issues even faster Design and evolve our service APIs as the platform grows Mentor other engineers and help set technical direction as the team scales Our Tech Stack Python / Django Postgres Temporal (workflow orchestration) PubSub GCP / AWS, including GCP Identity Platform and Vertex AI for model serving OpenTelemetry, Grafana Tempo / Jaeger GitLab Who You Are We are looking for an experienced backend engineer who is comfortable owning a problem end to end: evaluating a handful of possible technical approaches, shipping the one you picked, and operating it under real production load. You care about making systems observable and debuggable, not just functional. You have made infrastructure tradeoffs before, deciding what hardware, architecture, or vendor to use, and why, and you can back that decision with data. You enjoy mentoring other engineers and raising the technical bar for the team. 5+ years of professional experience as a backend engineer shipping software in the cloud Have evaluated and adopted workflow-orchestration or distributed-pipeline tooling (e.g. Temporal, Celery, Airflow, Dagster, or similar) for a production system Have hands-on experience with identity/auth systems (SSO, OAuth/OIDC, RBAC) and multi-tenant architectures Are comfortable with observability tooling (distributed tracing, metrics, structured logging) and have used it to debug production issues Are fluent in multiple languages, Python being your strongest Have first-hand experience with databases (relational and document-oriented), service-oriented architectures, cloud data pipelines (stream and batch), Docker, Kubernetes Are familiar

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