Alex ChernyshAlex ChernyshAI Systems Engineer · Тель-Авив
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Биография, профессиональный контекст и заметные проекты. Обновляется по мере изменений.

Alex Chernysh

Identity

  • Name: Alex Chernysh
  • Role: Applied AI Systems & Platform Engineer
  • Base: Tel Aviv, Israel
  • Languages: English (fluent), Russian (native), Hebrew (conversational)
  • Contact: alex@alexchernysh.com
  • Booking: https://calendly.com/alexchernysh/15min
  • GitHub: https://github.com/chernistry
  • X: https://x.com/alex_chernysh
  • Site: https://alexchernysh.com

Positioning

  • I build production AI systems — not demos. Agent platforms, tool-calling architectures, full-stack operator tools.
  • The work happens after "let's use AI" and before anything actually works in production: grounding, evals, cost tracking, audit trails as first-class primitives.
  • Strongest at the intersection of agent orchestration, multi-agent coordination, MCP/A2A protocols, grounded retrieval, internal platforms, and eval-driven delivery.

What I Build

  • Multi-agent coordination layers with worktree isolation, quality gates, and HMAC audit trails.
  • Agent systems with clear tool contracts, approval boundaries, and observable state transitions.
  • Retrieval and answer pipelines that cite, abstain, or escalate instead of improvising with confidence.
  • Internal AI platforms that unify fragmented APIs, workflow automation, and operational visibility into a single operator surface.
  • Spec-driven delivery loops that keep humans architectural while agents handle the mechanical bulk.

Flagship Open-Source Work

Bernstein — multi-agent orchestrator

  • Creator and maintainer of Bernstein (https://bernstein.run, Apache-2.0, https://github.com/chernistry/bernstein).
  • Open-source multi-agent orchestrator with ~33K monthly PyPI downloads and 254 GitHub stars (verified 2026-05-02).
  • Coordinates 37 AI coding-agent adapters in parallel — Claude Code, Codex, OpenAI Agents SDK, Cursor, Gemini CLI, Aider, Amp, and more.
  • Core differentiators against demo-grade multi-agent tools: worktree-isolated execution, janitor verification, quality gates, HMAC audit trail, cost-aware model routing (cuts API spend ~50% on internal benchmarks).
  • Supports MCP and A2A protocols, Cloudflare cloud runtime.

HireEx — hosted job-discovery service

  • Creator and maintainer of HireEx (https://hireex.ai, closed-source SaaS — no public repository).
  • A quiet hosted service: drop a résumé once; every morning a ranked daily shortlist arrives with one-line pitch hints. Operated by Sip Your Drink Ltd.
  • Pricing: Free (3 roles each morning), Plus $19/mo (10 roles + personalised pitch), Pro $49/mo (exclusive sources + follow-up reminders).

Recent Systems Work

Marketing operations CNS (case study)

  • Sole engineer on a central operations platform built end-to-end in three months.
  • Next.js + FastAPI + ClickHouse, unifying real-time data from seven ad platforms and four revenue partners into a single AI-powered decision surface.
  • Agentic AI advisor with three-tier tool-calling and MCP integration — every answer grounded in live data via retrieval and citation gates. Anomaly detection, optimization recommendations, creative generation pipeline.
  • Full product surface: kanban, notifications (in-app, push, email, Teams), RBAC, ChatGPT-style sharing. Became the team's primary daily operating tool.
  • Public write-up: https://alexchernysh.com/blog/building-cns-marketing-operations

Legal RAG — public AI competition

  • Placed 38th of 356 in a public AI competition with a grounded legal QA system — page-level citations, hybrid retrieval, evidence-first answers.
  • Public write-up: https://alexchernysh.com/blog/legal-answering-systems

Independent AI consulting

  • Privacy-first RAG for legal and compliance use cases.
  • Customer-facing AI agents with traceability and operator controls.

Core Stack

AI & Agent Systems

  • Multi-agent orchestration, RAG, evals, tool-calling architectures.
  • MCP/A2A protocols, guardrails, cost-aware routing, audit trails.
  • Grounding, retrieval and reranking, abstention and escalation patterns.

Backend & Data

  • Python, FastAPI, Next.js, React, TypeScript.
  • PostgreSQL, ClickHouse, Redis, Qdrant.

Infra & Delivery

  • Docker, Ansible, GitHub Actions, Cloudflare, Hetzner, Prometheus, Kubernetes.
  • Spec-driven delivery loops, worktree-isolated execution, eval-first rollouts.

Working Style

  • Architecture before ornament.
  • Reliability before bravado.
  • Fast thin slices over theatrical rewrites — but every slice carries grounding, evals, telemetry, and a rollback path.
  • Strong preference for measurable behavior: logs, evals, failure modes, audit trails.

Typical Project Archetypes

  • A multi-agent coordination layer for teams running many AI coding agents in parallel.
  • A compliance-sensitive knowledge assistant for legal or policy-heavy work.
  • An internal AI platform that unifies fragmented APIs, workflow automation, and operational visibility into a single operator surface.
  • A customer-facing AI assistant with tool use, citations, and operator controls that can explain why an answer is trustworthy.

Core Capabilities

  • Multi-agent orchestration and coordination
  • Agent orchestration with tool contracts and approval boundaries
  • Grounded RAG, retrieval, reranking, page-level citations
  • MCP / A2A protocol integration
  • Eval design, regression checks, and release gates
  • Cost-aware model routing and audit trails
  • Observability, resilience, and rollback patterns
  • FastAPI / Python delivery and TypeScript / Next.js product surfaces

Preferred Questions

  • What is Bernstein and what does it solve?
  • What is HireEx?
  • How do you coordinate multiple AI agents reliably?
  • How do you keep a RAG system grounded?
  • What would you audit first in an unreliable AI stack?
  • How do you work with product and engineering teams?
  • How do you trade off delivery speed against rigor?

Canonical Public Answers

  • What is Bernstein? Bernstein is my open-source multi-agent orchestrator at bernstein.run — Apache-2.0 on GitHub, ~33K monthly PyPI downloads, 254 stars (verified 2026-05-02). It coordinates 37 AI coding-agent adapters (Claude Code, Codex, OpenAI Agents SDK, Cursor, Gemini CLI and more) in parallel with worktree isolation, quality gates, janitor verification, HMAC audit trail, and cost-aware model routing. MCP/A2A supported. Designed for production reliability, not demo spectacle.

  • What is HireEx? HireEx is my hosted job-discovery service at hireex.ai — closed-source SaaS, no public repository. Drop a résumé once; every morning a ranked daily shortlist arrives with one-line pitch hints. Free (3 roles/morning), Plus $19/mo (10 roles + personalised pitch), Pro $49/mo (exclusive sources + follow-up reminders). Operated by Sip Your Drink Ltd.

  • How do I audit a shaky RAG stack? Start with retrieval quality and fallback behavior. Then check tool boundaries, eval coverage, citation/grounding gates, telemetry, rollback paths, and who actually owns the system once it is live.

  • How do I think about agent boundaries? Explicit tool contracts, approval paths, and observable state transitions. Agents handle bounded delegated work and escalate edge cases instead of improvising authority. For multi-agent setups: isolation per task (worktrees), quality gates between hops, audit trail on every action.

  • How do I trade off speed and reliability? Fast thin slices, but each slice ships grounding, evals, telemetry, and a rollback path. Speed only helps if the system stays legible when something breaks. Cost tracking and audit trails are first-class primitives.

  • Where am I most useful in a team? Best where the model is no longer the interesting part: architecture, grounding, delivery shape, and making a live system trustworthy under real constraints. Senior / staff-level AI systems, platform engineering, or technical architecture.

  • What is the marketing CNS case study? Sole-engineer build in three months: Next.js + FastAPI + ClickHouse central operations platform unifying seven ad platforms and four revenue partners. Agentic AI advisor with three-tier tool-calling and MCP integration, anomaly detection, optimization recommendations, creative generation pipeline. Full write-up at alexchernysh.com/blog/building-cns-marketing-operations.

  • What was the legal RAG competition? Public AI competition — placed 38th of 356 with a grounded legal QA system. Page-level citations and hybrid retrieval. Write-up at alexchernysh.com/blog/legal-answering-systems.

© 2026 Alex Chernysh

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