Matt Bosek

Engineering Lead // AI Search, Crawling, Data Platforms

Matt Bosek // Engineering Lead, product builder

15+ years building SaaS products, internal platforms, and startup systems from zero to production. Hands-on in architecture and coding, but equally comfortable leading teams, hiring, planning delivery, and making pragmatic product decisions under uncertainty.

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Navigation

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Agent 01 // Command Feed

interactive terminal, shortcuts, status trace

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Agent 02 // Profile Surface

Profile + fit

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Engineering lead who can own architecture, coding, hiring, and delivery in one seat.

I work best where product thinking, technical judgment, and execution speed need to stay tightly coupled. The value is not only in direction-setting but in personally carrying key implementation and architecture decisions through to launch.

Recent work has centered on data-intensive products that combine crawling, monitoring, structured extraction, queue-based processing, AI enrichment, and search-oriented product delivery.
TypeScript/Node.js, React, AWS, distributed teams, and AI-augmented engineering workflows designed to keep execution fast without making the stack brittle.

I raise engineering standards without stepping away from the code.

Agent 04 // Strength Cards

high-signal capabilities with direct hover feedback

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Agent 03 // Radar + Actions

focus map, exports, contact and quick access

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LeadBuildSearchDataOps

Radar focus

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Export MarkdownExport PDFmatt@mattbosek.comlinkedin.com/in/mattbosek

Agent 06 // Geiger Stack

expertise inventory, signal density and stack heat

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Expertise inventory

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Engineering leadershipTechnical strategyHiringMentoringRoadmap executionDistributed teamsOpenAIAnthropicRAGAgent workflowsStructured extractionAI automationWeb scrapingBrowser automation

Agent 07 // Otterly Fit

role match, ownership, execution style

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Why I Fit OtterlyAI

Monitoring, crawling, extraction, and search-oriented product delivery.

The strongest overlap is recent: multi-source monitoring, browser automation, queue-based processing, LLM enrichment, relevance filtering, dashboards, alerting, and data pipelines that turn noisy content into usable business signals.

Leadership Style

Player-coach engineering lead.

I set direction, remove ambiguity, raise standards, and still go hands-on where architecture quality or speed demands it. That model tends to work especially well in early-stage and growth-stage teams.

Technical Highlights

Adaptive crawlers, deduplication, AI normalization, worker architectures.

Adaptive scraping schedulers, Redis/database-backed deduplication, worker-based download/parse/enrich/notify layers, and lean AWS/Hetzner infrastructure shaped around startup constraints.