Target: remote-capable Junior Python Backend / API Automation. Adjacent lanes: QA/API Python, CRM/API Integration, Internal Tools, and Support Engineer with Python when the work is backend/API-heavy.

Experience base: Autoschool54 / DriveDesk backend and application-support work since March 2024, turned into public-safe work samples without exposing live production data; private production data replaced by synthetic evidence.

First code proof: OpsDesk Lite shows a backend/API slice with FastAPI, SQLAlchemy, idempotent integration outbox, pytest, Docker Compose, CI, and privacy audit. Real-work context: Autoschool Intake/Admin shows request intake -> backend validation -> database record -> admin queue -> operator status with private production data replaced by synthetic evidence.

First useful result: one FastAPI endpoint, data model, admin queue step, API/CRM mapping, pytest/API smoke check, SQL/data check, or runbook gap shipped as a reviewed slice with docs and handoff notes.

I build Python backend/API automation for messy business workflows: FastAPI/PostgreSQL services, admin queues, REST/OpenAPI boundaries, API/CRM integrations, QA/API checks, and handoff docs. DriveDesk and RAG/AI workflow evidence stay visible only after the backend/API fit is clear.

Delivery boundary: the useful signal is a reviewed FastAPI/PostgreSQL/Telegram/admin slice with tests, logs, docs, and handoff. Stack fit: Python, FastAPI, PostgreSQL, REST/OpenAPI, SQL, pytest/API smoke checks, Docker/Compose boundaries, GitHub Actions, docs, runbooks, and recovery notes.

Forwarding after fit is clear: Recruiter review pack, PDF resume, and ATS plain-text resume. For application parsers, vacancy-specific routing, and first messages: Role Fit Message Examples. Deeper technical review: DriveDesk AI Operator and AI Backend Review Pack.

Message me first when there is one messy workflow, one risky integration boundary, or one Python/backend automation, API/CRM, QA automation, AI workflow, or reliability slice that should become testable, logged, documented, and handed off.

First reply promise: I will answer with a fit read, risky assumptions, the smallest responsible first slice, the work sample to inspect, and the right next path: remote-capable role screen, technical review, scoped project, or no-fit.

Suggested recruiter path: GitHub recruiter handoff -> OpsDesk Lite -> LinkedIn Recruiter Packet -> First Backend Role Fit -> Autoschool Intake/Admin work sample -> PDF resume. Use the recruiter review pack only after the role fit is clear. Message on LinkedIn with the role title and one useful success condition; remote setup, stack/systems, timeline, and compensation band can follow after fit is clear.

LinkedIn support routes: LinkedIn Profile Signal for headline, About, Featured, Open-to-Work, and skills alignment; Backend/API Work Samples for the profile-view work-sample route; Recruiter Preferences for Open-to-Work filters; Decision-Ready Contact for first-contact context; and Skill Evidence for the skill-to-work-sample map.

AI-native delivery loop: I use AI tooling to compress discovery, implementation, debugging, docs, and review into faster iteration, while architecture, state boundaries, privacy checks, tests, logs, deployment, runbooks, and shipped quality remain my responsibility.

Search-fit titles: Junior Backend Developer, Junior Python Developer, Python Automation Engineer, Backend Automation Engineer, QA Automation Python, API Testing / Test Automation Engineer, Support Engineer with Python, Integration Engineer, CRM/API Integration Engineer, and Internal Tools Engineer.

LinkedIn recruiter search alignment: remote-capable or Russian-speaking full-time roles where Python, FastAPI, PostgreSQL, Docker, GitHub Actions, RAG, Vector Databases, CRM/ERP/API integration, QA Automation Python, OpenAPI, n8n, Telegram, or pgvector appear in the role.

Best immediate starts: Python/backend workflow slice, CRM/ERP/API adapter, internal operations tool, QA/API verification path, or reliability handoff. AI workflow automation is a differentiator after backend/API ownership is established.

DriveDesk AI Operator is deeper backend/AI workflow evidence after the first-role backend/API fit is clear: documents, call audio, transcripts, or CRM leads -> RAG, analysis, scoring, tasks, Telegram approval, and CRM handoff. The larger DriveDesk Flagship Backend Workflow is the broader backend workflow path.

Since March 2024, I have supported Autoschool54 backend/application-support work remotely while turning real operational problems into public-safe backend workflow samples. Key pinned repos: OpsDesk Lite, DriveDesk Core, AI Ops Workflow Kit with business scenario replay, first-slice playbook, and live PostgreSQL/pgvector persistence, DeployMate, and MPlusForm.

Fast fit checklist: message me when the work is remote-capable or async-reviewed and needs a backend-owned AI workflow, CRM/ERP/API integration, internal operations tool, QA/API verification path, Docker/CI handoff, or one first working slice with tests, logs, docs, and a handoff route. I am not positioning for onsite-only roles, pure prompt/content tasks, isolated static websites, student/course assignments, standalone game clones, generic mobile/ecommerce apps without backend/integration ownership, or vague work where success cannot be defined.

LinkedIn: linkedin.com/in/alex-gerlitz-a659ab3bb · LinkedIn Services