BENCH+ PROFILE

Aleksandr

AI Integration Engineer
Product-oriented LLM, semantic search, RAG, and full-stack MVP integration

Hands-on product and engineering profile suited to AI-enabled product integration, MVP delivery, and stakeholder-facing solution work, with a long background in live digital products and backend-supported systems.

BEST-FIT USE CASE

Building and iterating AI-enabled web products where discovery, requirements shaping, LLM/RAG integration, full-stack implementation, release preparation, and practical validation are handled close to the business problem.

Not the primary fit: Pure model research, dedicated production MLOps ownership, or narrowly scoped task execution without product context would be a weaker fit.

EVIDENCE HIGHLIGHTS

What supports the assessment

Built a conference networking web application as a PWA with a React/Vite frontend and Node.js/PostgreSQL backend, taking it through customer approval and pre-launch iteration.

Integrated LLMs, semantic search, and RAG into product features during independent product work.

Worked on analytical SaaS and internal analytical platforms involving ML-based LTV improvement, StarRocks-based analytics, dashboards, and RAG use cases.

Used AI-assisted development workflows while retaining control over architecture, generated code quality, business logic, and release checks.

Designed and maintained solution architecture and infrastructure for AI-enabled products and analytical platforms.

Earlier hands-on engineering covered client-server development, backend services, and infrastructure supporting live digital products.

Led product delivery for mobile game products across concept, prototype, release, post-release updates, monetization, analytics-driven iteration, and long-term operations.

WORKING STYLE

How they show up

Uses PRD-driven task decomposition and context updates when working with AI-assisted implementation.
Reviews generated code manually, commits changes personally, checks alignment with architecture, and corrects overcomplicated solutions.
Approaches technology selection through total cost of ownership, maintainability, team availability, infrastructure constraints, scale, and compliance needs.
Values direct problem solving for concrete users and business needs, with a preference for measuring outcomes.

KEY STRENGTHS

Connects stakeholder problems to hypotheses, requirements, MVP scope, and working implementations.
Has hands-on experience integrating LLM features, semantic search, and RAG into product functionality.
Combines frontend, backend, API, database, and release-oriented implementation in product contexts.
Applies practical validation of AI-assisted implementation through manual review, local and staging checks, smoke tests, targeted automated tests, and rollback when needed.
Has experience with full-cycle digital products from concept and prototype through launch preparation, live operation, and iterative improvement.
Brings product leadership background across cross-functional engineering, QA, design, localization, publishing, and marketing collaboration.

PRIMARY SKILLS

LLM integrationRetrieval-Augmented GenerationSemantic searchReactNode.jsPostgreSQLAPI developmentProduct discovery

ADDITIONAL TECHNOLOGIES

VitePWADockerPlaywrightMongoDBRedisUnityProduct analyticsA/B testingAgile workflows

INDUSTRY EXPERIENCE

AI / DataSaaSGamingSocial / Community

ROLE MATCH

AI Integration EngineerStrong

Technical Product ManagerPartial

Full-stack EngineerPartial