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BENCH+ PROFILE

Mikhail

AI Product / Transformation Lead
B2B AI assistant platforms, RAG-enabled automation, enterprise integration, and product-to-delivery leadership with a strong backend systems foundation

Lead-level AI product and technical delivery profile combining B2B AI assistant product strategy, hands-on implementation, RAG workflows, enterprise integrations, and long-running backend architecture experience in regulated and data-intensive environments.

BEST-FIT USE CASE

Best suited for a full-cycle B2B AI transformation or AI assistant platform role where product direction, stakeholder translation, architecture, hands-on backend implementation, RAG integration, delivery control, and customer-facing technical solution work are combined.

Not the primary fit: Less aligned with roles focused only on routine documentation, dedicated testing, pure infrastructure operations, or isolated maintenance without product, customer, or architecture involvement.

EVIDENCE HIGHLIGHTS

What supports the assessment

01

Co-founded and hands-on built a B2B AI assistant platform from market analysis and MVP definition through architecture, implementation, customer pilots, and production use.

02

Designed production RAG, hybrid vector and lexical search, Python-agent, and enterprise-integration workflows for an AI assistant system.

03

Developed an AI tool and RAG platform for document analysis and recommendations in a front-office financial environment.

04

Built asynchronous message-exchange services between event-streaming and enterprise messaging systems.

05

Led architecture and development for distributed crypto trading, staking, lending, reporting, and banking-integration platforms.

06

Developed diagnostic software capabilities for medical equipment using event-driven architecture and automated-testing APIs in a regulated environment.

07

Developed analytics, reporting, forecasting, and sentiment-analysis capabilities using market data and cognitive services.

08

Interview discussion covered model choice by user type and economics, batching, rate limits, separate AI review, and avoiding large AI-generated diffs without review.

WORKING STYLE

How they show up

  • Prefers full-cycle product and technical roles involving client value, product delivery, implementation, and business contact.
  • Works effectively in small and remote teams and has described collaboration with design, testing, consulting, and customer-facing participants.
  • Uses structured review for AI-generated code, including separate review context, git-change analysis, human review, and limiting overly broad changes.
  • Frames technology choices around total cost of ownership, product lifespan, budget, service-level needs, team availability, infrastructure constraints, scale, compliance, and maintainability.
  • Describes planning as a lightweight but controlled process: business case, technical plan, estimate, peer validation, baseline plan, iterative adjustment, and progress review.