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BENCH+ PROFILE

Nikolai

AI Engineer
Agentic LLM systems, RAG workflows, and full-stack AI product delivery

Senior AI engineer with full-stack delivery background, recent hands-on production-oriented work on LLM agents, RAG pipelines, workflow automation, and healthcare documentation systems.

BEST-FIT USE CASE

Building AI-first SaaS features that combine LLM workflows, backend APIs, frontend product flows, asynchronous processing, cloud deployment support, and stakeholder feedback loops from MVP to production-oriented rollout.

Not the primary fit: Roles focused mainly on static frontend markup, pure people management, dedicated DevOps ownership, or narrow model research without product engineering responsibilities.

EVIDENCE HIGHLIGHTS

What supports the assessment

01

Implemented an AI-assisted medical documentation system and developed RAG pipelines and LLM agents for structured data processing.

02

Created a no-code workflow automation platform using AI and asynchronous processing.

03

Delivered production-oriented AI MVP increments with responsibility across backend services, frontend applications, AI architecture, and delivery quality.

04

Integrated an AI assistant into a real-time web and mobile messaging system while contributing to architecture and infrastructure design.

05

Developed full-stack e-commerce catalog, product detail, filtering, analytics, performance, and design-system functionality.

06

Designed frontend architecture and developed core social, communication, notification, and specialized editor functionality for an education SaaS platform.

07

Used AI-assisted development with human review of generated code and CI/CD checks before deployment.

08

Collaborated directly with stakeholders and product teams on requirements, experiments, metrics, UX improvements, and delivery trade-offs.

WORKING STYLE

How they show up

  • Can work both independently and in a team, with experience as a founding-style engineer, solo developer, and technical lead.
  • Communicates directly with stakeholders and clients and links technical choices to product and business problems.
  • Uses Agile and Kanban-style delivery practices.
  • Raises recurring requirement-quality issues for team discussion and tracks problematic tasks through a board.
  • Reviews generated code before deployment and applies human-in-the-loop checks in the development process.