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BENCH+ PROFILE

Felix

AI Integration Engineer
LLM-enabled product engineering, agentic workflow integration, API-backed web applications, and AI-assisted delivery from product specification through deployment

Hands-on AI product builder with a product-management background and recent implementation of production LLM-enabled applications, agentic workflows, backend APIs, deployment automation, and evaluation tooling. Best aligned with roles that combine stakeholder clarification, AI workflow integration, web application delivery, and operational ownership.

BEST-FIT USE CASE

Building and productizing AI-enabled internal tools, automation platforms, conversational applications, or workflow assistants where requirements need to be clarified, LLM APIs integrated, backend and frontend components implemented, and the system deployed into a working environment.

Not the primary fit: Less aligned with narrow roles focused only on model research, pure product strategy without hands-on implementation, or large-scale platform operations where infrastructure engineering is the sole responsibility.

EVIDENCE HIGHLIGHTS

What supports the assessment

01

Implemented a standalone AI assistant service that accepts natural-language requests, analyzes a codebase, runs coding tools in isolated worktrees, creates pull requests, and supports preview and deployment workflows.

02

Built streaming chat, authentication, session management, build-status APIs, authorization, access control, request-complexity classification, and token-usage tracking for an AI assistant service.

03

Created cloud infrastructure and deployment configuration for an AI assistant service using containerization, persistent storage, scheduled events, server resources, secrets management, and monitoring components.

04

Delivered a cashless venue-management proof of concept with backend APIs, administrative and point-of-sale interfaces, payment-related workflows, auditing, API documentation, containerized development, and database integration tests.

05

Built a production job-search automation web application with vacancy discovery, scoring logic, generated text output, asynchronous scrapers, database-backed state, a user interface, deployment, and operational setup.

06

Built a production conversational AI application with multi-LLM orchestration, speech recognition, structured JSON extraction, matching logic, real-time UI, authentication, profile scoring, and prompt-quality evaluation.

07

Moved a local-only MVP toward a pre-production state by clarifying direction with a client, fixing product and technical issues, testing, deploying to separate hosts, and adding auto-deployment.

08

Interview discussion described practical review of AI-generated code using documentation generation, tests, separate model checks, product-logic review, and multiple models for planning and verification.

WORKING STYLE

How they show up

  • Works end-to-end across product clarification, architecture, implementation, debugging, deployment, and operations.
  • Uses structured planning before implementation and applies AI coding tools as part of the development workflow.
  • Documents decisions, rationale, technical details, and delivery progress for client-facing work.
  • Reviews generated code through tests, documentation, product-logic checks, and additional model-based review passes.
  • Has recently worked solo on several products while also expressing preference for communication, discussion, and collaboration in a team setting.
  • Responded positively to autonomous delivery expectations and has experience working directly with clients.