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BENCH+ PROFILE

Viktor

Technical Product Manager
B2B SaaS platform, CRM workflow, and AI-enabled product delivery

Senior technical product profile with strong B2B SaaS, communications-platform, CRM workflow, AI-assisted delivery, quality automation, and cross-functional launch experience.

BEST-FIT USE CASE

Technical product ownership for a B2B SaaS platform where complex workflows, integrations, AI-enabled internal tooling, rollout coordination, and engineering-facing requirements need to be aligned across product and technical teams.

Not the primary fit: Pure hands-on software implementation roles, dedicated model-training roles, or narrowly scoped design-only positions would not match the strongest demonstrated responsibility pattern.

EVIDENCE HIGHLIGHTS

What supports the assessment

01

Owned core areas of a B2B SaaS communications platform, including platform workflows, infrastructure-facing delivery, regional rollout coordination, and AI-enabled internal tooling.

02

Led an AI track for internal assistants and voice workflows using retrieval, tool integrations, MCP-style orchestration, voice generation, and specification-based testing.

03

Owned CRM sales-flow product scope covering lead qualification, proposal preparation, service approval, signing, billing, catalog logic, and external integrations.

04

Coordinated technical and business teams during a billing-platform migration and designed more flexible catalog-driven CRM behavior.

05

Led MVP and quality delivery for mobile augmented-reality and computer-vision navigation systems, including labeled-data methodology and automated quality checks.

06

Worked across business analysis, test analysis, and QA by preparing requirements, mockups, test strategies, and detailed test cases.

07

Earlier hands-on QA automation included billing checks with database scripting and Java, test-environment automation, and continuous-test system maintenance.

WORKING STYLE

How they show up

  • Describes product work through business goals, technical constraints, implementation plans, validation steps, and stakeholder alignment.
  • Uses structured documentation and repository-based context to guide AI-assisted development, testing, and review workflows.
  • Approaches technical choices by weighing lifecycle, budget, maintainability, team availability, infrastructure constraints, security, and scale.
  • Frames stakeholder disagreement around alternatives, trade-offs, and business outcomes rather than direct contradiction.
  • Coordinates through regular checkpoints, intermediate-result review, risk discussion, and plan updates as requirements change.