Why rinf.tech?

Why do we do what we do?

We inspire one another to share our tech-works in this amazing and abundant world. So we became developers, innovators, thinkers, software builders, and hardware makers!

Our Vision!

Founded in 2006 with 650+ engineers & global presence (8 delivery centers in Europe & North America) we strive to become a leading East-European technology partner for growing organizations in need of digital transformation of their products and services!

What you’ll do

• Act as the primary business-facing advisor and partner to the client’s Squad Lead.
• Analyze customer journeys, personalization gaps, business KPIs, and operational maturity.
• Translate retail objectives (personalization, loyalty value, category uplift, omnichannel relevance) into functional requirements for the recommender system.
• Define the value cases and success metrics for modernization.
• Evaluate the maturity and limitations of existing recommender systems and their supporting architecture.
• Evaluate gaps in the current recommender engine from a business and functional standpoint.
• Identify risks related to fragmented solutions, rework, and inconsistent recommendation logic.
• Provide guidance on whether the current architecture should be maintained, modernized, or rebuilt.
• Define and advise what a consolidated, scalable recommendation ecosystem should look like for the client.
• Shape the long-term vision for a unified platform that ensures consistency, quality, and measurability across all business units.
• Work with the Squad Lead, AI and Data Consultants to align on technical feasibility, data foundations, and use case prioritization.
• Map use cases across retail, wholesale, loyalty, promotion engines, lifecycle management, and omnichannel activation.
• Connect business goals—conversion, retention, RMF segments, frequency, customer lifetime value—to the capabilities of modern recommender systems.
• Help stakeholders understand the impact of harmonized logic and shared infrastructure on commercial performance.
• Analyze customer interactions across online, store, app, loyalty, and promotional paths.
• Identify where current recommendations fail to deliver relevance or timing.
• Recommend improvements to ensure consistent personalization across all customer touchpoints
• Bridge the gap between commercial teams, product, and the technical squad.
• Clarify business implications of technical decisions
• Ensure feasibility and alignment with operational processes.
• Drive clarity and reduce trial-and-error during implementation.

What you need to be successful

• Strong hands-on experience with customer recommendation systems in retail or wholesale (7–12+ years).
• Experience evaluating or designing recommendation architectures, particularly in cloud environments or distributed systems.
• Deep knowledge of loyalty optimization, promotion engines, and omnichannel personalization.
• Solid understanding of retail customer journeys, segmentation, and data-driven engagement strategies.
• Comfortable working with AI, data, and platform teams to translate business value into technical requirements.
• Proven ability to assess solution gaps, identify modernization paths, and guide stakeholders through change.

Join the #PeopleofManyTalents #EngineerOfTheFuture