Knowledge of German at the C1 level is required.
Role Overview
We are looking for a Delivery Manager/Technical Delivery Manager to lead deployments of AI/Computer Vision solutions across retail environments (Self-Checkout/SCO).
The DM will own end-to-end implementation activities, including customer onboarding, POS integrations, infrastructure readiness, model release operations, installation coordination, monitoring, and operational support.
Day-to-Day Responsibilities
- Launch new store deployments and coordinate customer onboarding.
- Coordinate and validate POS integrations.
- Validate network and infrastructure readiness; coordinate VPN/firewall setup with customer IT.
- Prepare installation work orders and coordinate camera and PU installation.
- Manage store-level configuration changes via GitLab.
- Execute model release operations: review metrics, make Go/No-Go decisions, coordinate rollout.
- Monitor dashboards, logs, and KPI reporting; investigate alarms and incidents.
- Prepare operational reports (customer-facing and internal).
- Maintain store inventory records and IP matrices.
- Provide support and manage incident responses (SLA & customer inquiries).
Core Requirements
Technical Skills
- German, English (C1 and higher), or Russian as a bonus.
Understanding of:
- SDLC in Computer Vision products.
- Networking fundamentals: LAN/WAN, DHCP vs Static IP, VPN concepts, firewall rules, and ports.
- Comfortable validating store infrastructure readiness, coordinating VPN/firewall setup, and troubleshooting connectivity issues with customer IT teams.
- Linux workstations/edge devices: service checks, log retrieval, basic container operations (Docker).
- Remote edge infrastructure operations.
- Text configuration, JSON—reading, editing, merging configurations.
Experience with:
- POS/API integrations and other API integrations.
- Reading technical documentation, API testing, and validation.
- Emulator-based testing, end-to-end request/response validation.
- Acceptance testing.
- Independently manage GitLab MRs for configuration changes.
Candidate should be capable of independently driving integrations and escalating only true L3/code-level issues.
ML/Model Operations
- The DM is the primary owner of model release decisions. This does not require writing ML code but does require operational confidence with CV metrics and release workflows.
Required:
- AI tools (Claude, GPT, etc.).
- Basic understanding of model evaluation metrics relevant to detection tasks (precision, recall, alarm rate, threshold behavior).
- Ability to compare model versions using pre-built validation reports (Google Sheets/Confluence release notes) and make Go/No-Go decisions.
- Coordination of canary deployments (2–3 stores) before full rollout.
Cloud & Data Platforms
Basic working knowledge of:
- Google Cloud Platform (GCP): GCS bucket navigation.
- SQL/NoSQL DB—no complex SQL.
- Credentials handling and service account basics.
Required Background
- 2–4 years in roles such as: Technical Project Manager, Delivery Manager, Customer Engineer, Technical Operations Manager.
- Retail/SaaS deployment experience strongly preferred over a pure project management background.
- Experience managing multi-site rollouts.
Strong candidates demonstrate:
- Ownership mindset and a high level of independence.
- Structured communication and strong follow-through.
- Ability to work under pressure and manage production incidents.
- Excellent stakeholder communication (both engineering teams and customers).
- Strong documentation discipline.
Expected to:
- Maintain Confluence/Jira documentation, create and improve runbooks/playbooks.
- Coordinate cross-functional teams and escalate issues clearly with proper context and evidence.
- Maintain store infrastructure records, IP inventories, and deployment trackers.
Nice-to-Have
- Retail/self-checkout experience.
- Managing Computer Vision projects.
- Camera systems knowledge: ROI setup, alignment, FOV validation.
- Familiarity with DevOps workflows and GitLab.
Ideal Candidate
We are looking for someone who:
- Can independently drive deployments to production across multiple customers and countries.
- Is comfortable working with infrastructure, logs, and configuration management.
- Understands ML model release workflows and can make operational decisions on model quality.
- Communicates effectively with both engineering teams and customers.
- Owns non-code execution without delegating operational tasks to engineers.
- Treats production as their personal responsibility.
Why work with us
You will help bring AI and computer vision products from development into real-world retail deployments. The role combines a bit of technical delivery, customer-facing work, and coordination across product, engineering, and implementation teams.
This is a hands-on role for someone who wants to be close to real-world and production operations—not just project tracking.